{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objective\n",
    "* 20190102: \n",
    "    * Predict stock price in next day using long short term memory(LSTM)\n",
    "    * Given prices for the last N days, we do prediction for day N+1\n",
    "    * Here we split 3 years of data into train(60%), dev(20%) and test(20%)\n",
    "* 20190120 - Diff from SotckPricePrediction_v4_lstm.ipynb: \n",
    "    * Instead of MinMaxScaler, here we use StandardScaler\n",
    "    * Instead of using the same mean and variance to do scaling for train, dev and test sets, we scale the train set to have mean 0 and var 1, and then whenever we do prediction on dev or test set we scale the previous N values to also have mean 0 and var 1 (ie. use the means and variances of the previous N values to do scaling. We do this for both feature columns and target columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import math\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import time\n",
    "\n",
    "from datetime import date\n",
    "from matplotlib import pyplot as plt\n",
    "from numpy.random import seed\n",
    "from pylab import rcParams\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from tqdm import tqdm_notebook\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from tensorflow import set_random_seed\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM\n",
    "from keras.utils import plot_model\n",
    "\n",
    "#### Input params ##################\n",
    "stk_path = \"./data/VTI.csv\"\n",
    "test_size = 0.2                # proportion of dataset to be used as test set\n",
    "cv_size = 0.2                  # proportion of dataset to be used as cross-validation set\n",
    "\n",
    "N = 9                          # for feature at day t, we use lags from t-1, t-2, ..., t-N as features. \n",
    "                               # initial value before tuning\n",
    "lstm_units=50                  # lstm param. initial value before tuning.\n",
    "dropout_prob=1                 # lstm param. initial value before tuning.\n",
    "optimizer='adam'               # lstm param. initial value before tuning.\n",
    "epochs=1                       # lstm param. initial value before tuning.\n",
    "batch_size=1                   # lstm param. initial value before tuning.\n",
    "\n",
    "model_seed = 100\n",
    "\n",
    "fontsize = 14\n",
    "ticklabelsize = 14\n",
    "####################################\n",
    "\n",
    "# Set seeds to ensure same output results\n",
    "seed(101)\n",
    "set_random_seed(model_seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Common functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_mape(y_true, y_pred): \n",
    "    \"\"\"\n",
    "    Compute mean absolute percentage error (MAPE)\n",
    "    \"\"\"\n",
    "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "def get_x_y(data, N, offset):\n",
    "    \"\"\"\n",
    "    Split data into x (features) and y (target)\n",
    "    \"\"\"\n",
    "    x, y = [], []\n",
    "    for i in range(offset, len(data)):\n",
    "        x.append(data[i-N:i])\n",
    "        y.append(data[i])\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    return x, y\n",
    "\n",
    "def get_x_scaled_y(data, N, offset):\n",
    "    \"\"\"\n",
    "    Split data into x (features) and y (target)\n",
    "    We scale x to have mean 0 and std dev 1, and return this.\n",
    "    We do not scale y here.\n",
    "    Inputs\n",
    "        data     : pandas series to extract x and y\n",
    "        N\n",
    "        offset\n",
    "    Outputs\n",
    "        x_scaled : features used to predict y. Scaled such that each element has mean 0 and std dev 1\n",
    "        y        : target values. Not scaled\n",
    "        mu_list  : list of the means. Same length as x_scaled and y\n",
    "        std_list : list of the std devs. Same length as x_scaled and y\n",
    "    \"\"\"\n",
    "    x_scaled, y, mu_list, std_list = [], [], [], []\n",
    "    for i in range(offset, len(data)):\n",
    "        mu_list.append(np.mean(data[i-N:i]))\n",
    "        std_list.append(np.std(data[i-N:i]))\n",
    "        x_scaled.append((data[i-N:i]-mu_list[i-offset])/std_list[i-offset])\n",
    "        y.append(data[i])\n",
    "    x_scaled = np.array(x_scaled)\n",
    "    y = np.array(y)\n",
    "    \n",
    "    return x_scaled, y, mu_list, std_list\n",
    "\n",
    "def train_pred_eval_model(x_train_scaled, \\\n",
    "                          y_train_scaled, \\\n",
    "                          x_cv_scaled, \\\n",
    "                          y_cv, \\\n",
    "                          mu_cv_list, \\\n",
    "                          std_cv_list, \\\n",
    "                          lstm_units=50, \\\n",
    "                          dropout_prob=0.5, \\\n",
    "                          optimizer='adam', \\\n",
    "                          epochs=1, \\\n",
    "                          batch_size=1):\n",
    "    '''\n",
    "    Train model, do prediction, scale back to original range and do evaluation\n",
    "    Use LSTM here.\n",
    "    Returns rmse, mape and predicted values\n",
    "    Inputs\n",
    "        x_train_scaled  : e.g. x_train_scaled.shape=(451, 9, 1). Here we are using the past 9 values to predict the next value\n",
    "        y_train_scaled  : e.g. y_train_scaled.shape=(451, 1)\n",
    "        x_cv_scaled     : use this to do predictions \n",
    "        y_cv            : actual value of the predictions\n",
    "        mu_cv_list      : list of the means. Same length as x_scaled and y\n",
    "        std_cv_list     : list of the std devs. Same length as x_scaled and y \n",
    "        lstm_units      : lstm param\n",
    "        dropout_prob    : lstm param\n",
    "        optimizer       : lstm param\n",
    "        epochs          : lstm param\n",
    "        batch_size      : lstm param\n",
    "    Outputs\n",
    "        rmse            : root mean square error\n",
    "        mape            : mean absolute percentage error\n",
    "        est             : predictions\n",
    "    '''\n",
    "    # Create the LSTM network\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=lstm_units, return_sequences=True, input_shape=(x_train_scaled.shape[1],1)))\n",
    "    model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "    model.add(LSTM(units=lstm_units))\n",
    "    model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "    model.add(Dense(1))\n",
    "\n",
    "    # Compile and fit the LSTM network\n",
    "    model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "    model.fit(x_train_scaled, y_train_scaled, epochs=epochs, batch_size=batch_size, verbose=0)\n",
    "    \n",
    "    # Do prediction\n",
    "    est_scaled = model.predict(x_cv_scaled)\n",
    "    est = (est_scaled * np.array(std_cv_list).reshape(-1,1)) + np.array(mu_cv_list).reshape(-1,1)\n",
    "    \n",
    "    # Calculate RMSE and MAPE\n",
    "#     print(\"x_cv_scaled = \" + str(x_cv_scaled))\n",
    "#     print(\"est_scaled = \" + str(est_scaled))\n",
    "#     print(\"est = \" + str(est))\n",
    "    rmse = math.sqrt(mean_squared_error(y_cv, est))\n",
    "    mape = get_mape(y_cv, est)\n",
    "    \n",
    "    return rmse, mape, est"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>107.510002</td>\n",
       "      <td>107.660004</td>\n",
       "      <td>107.250000</td>\n",
       "      <td>107.470001</td>\n",
       "      <td>101.497200</td>\n",
       "      <td>1820300</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>107.589996</td>\n",
       "      <td>107.760002</td>\n",
       "      <td>107.220001</td>\n",
       "      <td>107.629997</td>\n",
       "      <td>101.648300</td>\n",
       "      <td>552400</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>107.779999</td>\n",
       "      <td>107.849998</td>\n",
       "      <td>107.110001</td>\n",
       "      <td>107.169998</td>\n",
       "      <td>101.213867</td>\n",
       "      <td>3618100</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>107.589996</td>\n",
       "      <td>108.209999</td>\n",
       "      <td>107.370003</td>\n",
       "      <td>108.180000</td>\n",
       "      <td>102.167740</td>\n",
       "      <td>2443600</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>108.099998</td>\n",
       "      <td>108.269997</td>\n",
       "      <td>106.879997</td>\n",
       "      <td>107.050003</td>\n",
       "      <td>101.100533</td>\n",
       "      <td>2937200</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date        open        high         low       close   adj_close  \\\n",
       "0 2015-11-25  107.510002  107.660004  107.250000  107.470001  101.497200   \n",
       "1 2015-11-27  107.589996  107.760002  107.220001  107.629997  101.648300   \n",
       "2 2015-11-30  107.779999  107.849998  107.110001  107.169998  101.213867   \n",
       "3 2015-12-01  107.589996  108.209999  107.370003  108.180000  102.167740   \n",
       "4 2015-12-02  108.099998  108.269997  106.879997  107.050003  101.100533   \n",
       "\n",
       "    volume  month  \n",
       "0  1820300     11  \n",
       "1   552400     11  \n",
       "2  3618100     11  \n",
       "3  2443600     12  \n",
       "4  2937200     12  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(stk_path, sep = \",\")\n",
    "\n",
    "# Convert Date column to datetime\n",
    "df.loc[:, 'Date'] = pd.to_datetime(df['Date'],format='%Y-%m-%d')\n",
    "\n",
    "# Change all column headings to be lower case, and remove spacing\n",
    "df.columns = [str(x).lower().replace(' ', '_') for x in df.columns]\n",
    "\n",
    "# Get month of each sample\n",
    "df['month'] = df['date'].dt.month\n",
    "\n",
    "# Sort by datetime\n",
    "df.sort_values(by='date', inplace=True, ascending=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PwB9/AEOGHPHquUYP2eWXA5s2ybFjz1l12rSp/pqnc87WrZN9PKdMAcaMAT79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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = df.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split into train, dev and test sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use lags up to N number of days to use as features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_train = 453\n",
      "num_cv = 151\n",
      "num_test = 151\n",
      "train.shape = (453, 2)\n",
      "cv.shape = (151, 2)\n",
      "train_cv.shape = (604, 2)\n",
      "test.shape = (151, 2)\n"
     ]
    }
   ],
   "source": [
    "# Get sizes of each of the datasets\n",
    "num_cv = int(cv_size*len(df))\n",
    "num_test = int(test_size*len(df))\n",
    "num_train = len(df) - num_cv - num_test\n",
    "print(\"num_train = \" + str(num_train))\n",
    "print(\"num_cv = \" + str(num_cv))\n",
    "print(\"num_test = \" + str(num_test))\n",
    "\n",
    "# Split into train, cv, and test\n",
    "train = df[:num_train][['date', 'adj_close']]\n",
    "cv = df[num_train:num_train+num_cv][['date', 'adj_close']]\n",
    "train_cv = df[:num_train+num_cv][['date', 'adj_close']]\n",
    "test = df[num_train+num_cv:][['date', 'adj_close']]\n",
    "\n",
    "print(\"train.shape = \" + str(train.shape))\n",
    "print(\"cv.shape = \" + str(cv.shape))\n",
    "print(\"train_cv.shape = \" + str(train_cv.shape))\n",
    "print(\"test.shape = \" + str(test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaler.mean_ = [109.09217119]\n",
      "scaler.var_ = [95.19902222]\n",
      "x_train_scaled.shape = (444, 9, 1)\n",
      "y_train_scaled.shape = (444, 1)\n"
     ]
    }
   ],
   "source": [
    "# Converting dataset into x_train and y_train\n",
    "# Here we only scale the train dataset, and not the entire dataset to prevent information leak\n",
    "scaler = StandardScaler()\n",
    "train_scaled = scaler.fit_transform(np.array(train['adj_close']).reshape(-1,1))\n",
    "print(\"scaler.mean_ = \" + str(scaler.mean_))\n",
    "print(\"scaler.var_ = \" + str(scaler.var_))\n",
    "\n",
    "# Split into x and y\n",
    "x_train_scaled, y_train_scaled = get_x_y(train_scaled, N, N)\n",
    "print(\"x_train_scaled.shape = \" + str(x_train_scaled.shape)) # (446, 7, 1)\n",
    "print(\"y_train_scaled.shape = \" + str(y_train_scaled.shape)) # (446, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_cv_scaled.shape = (151, 9, 1)\n",
      "y_cv.shape = (151, 1)\n",
      "len(mu_cv_list) = 151\n",
      "len(std_cv_list) = 151\n"
     ]
    }
   ],
   "source": [
    "# Scale the cv dataset\n",
    "# Split into x and y\n",
    "x_cv_scaled, y_cv, mu_cv_list, std_cv_list = get_x_scaled_y(np.array(train_cv['adj_close']).reshape(-1,1), N, num_train)\n",
    "print(\"x_cv_scaled.shape = \" + str(x_cv_scaled.shape))\n",
    "print(\"y_cv.shape = \" + str(y_cv.shape))\n",
    "print(\"len(mu_cv_list) = \" + str(len(mu_cv_list)))\n",
    "print(\"len(std_cv_list) = \" + str(len(std_cv_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scaler_final.mean_ = [115.38294545]\n",
      "scaler_final.var_ = [195.78042751]\n",
      "x_test_scaled.shape = (151, 9, 1)\n",
      "y_test.shape = (151, 1)\n",
      "len(mu_test_list) = 151\n",
      "len(std_test_list) = 151\n"
     ]
    }
   ],
   "source": [
    "# Here we scale the train_cv set, for the final model\n",
    "scaler_final = StandardScaler()\n",
    "train_cv_scaled_final = scaler_final.fit_transform(np.array(train_cv['adj_close']).reshape(-1,1))\n",
    "print(\"scaler_final.mean_ = \" + str(scaler_final.mean_))\n",
    "print(\"scaler_final.var_ = \" + str(scaler_final.var_))\n",
    "\n",
    "# # Scale the test dataset\n",
    "# x_test_scaled, y_test, mu_test_list, std_test_list = get_x_scaled_y(np.array(df['adj_close']).reshape(-1,1), N, num_train+num_cv)\n",
    "# print(\"x_test_scaled.shape = \" + str(x_test_scaled.shape))\n",
    "# print(\"y_test.shape = \" + str(y_test.shape))\n",
    "# print(\"len(mu_test_list) = \" + str(len(mu_test_list)))\n",
    "# print(\"len(std_test_list) = \" + str(len(std_test_list)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create and fit the LSTM network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      " - 10s - loss: 0.0565\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1a3424c048>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the LSTM network\n",
    "model = Sequential()\n",
    "model.add(LSTM(units=lstm_units, return_sequences=True, input_shape=(x_train_scaled.shape[1],1)))\n",
    "model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "model.add(LSTM(units=lstm_units))\n",
    "model.add(Dropout(dropout_prob)) # Add dropout with a probability of 0.5\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(loss='mean_squared_error', optimizer=optimizer)\n",
    "model.fit(x_train_scaled, y_train_scaled, epochs=epochs, batch_size=batch_size, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_3 (LSTM)                (None, 9, 50)             10400     \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 9, 50)             0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 50)                20200     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 50)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 51        \n",
      "=================================================================\n",
      "Total params: 30,651\n",
      "Trainable params: 30,651\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Print model summary\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
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     "execution_count": 110,
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    }
   ],
   "source": [
    "# Plot model and save to file\n",
    "from IPython.display import SVG\n",
    "from keras.utils import plot_model\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "\n",
    "plot_model(model, to_file='model_v4a.png')\n",
    "SVG(model_to_dot(model).create(prog='dot', format='svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict on dev set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "est.shape = (151, 1)\n",
      "RMSE = 2.221\n",
      "MAPE = 1.134%\n"
     ]
    }
   ],
   "source": [
    "# Do prediction\n",
    "est_scaled = model.predict(x_cv_scaled)\n",
    "est = (est_scaled * np.array(std_cv_list).reshape(-1,1)) + np.array(mu_cv_list).reshape(-1,1)\n",
    "print(\"est.shape = \" + str(est.shape))\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse_bef_tuning = math.sqrt(mean_squared_error(y_cv, est))\n",
    "print(\"RMSE = %0.3f\" % rmse_bef_tuning)\n",
    "\n",
    "# Calculate MAPE\n",
    "mape_pct_bef_tuning = get_mape(y_cv, est)\n",
    "print(\"MAPE = %0.3f%%\" % mape_pct_bef_tuning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
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P8b59+9rs3xtv/zta+117C/YCsGrtKgpiCsguyfZcOzvybL7//nsOFRszbT/a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\nKSD/D+AgMwRwRBktAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "est_df = pd.DataFrame({'est': est.reshape(-1), \n",
    "                       'y_cv': y_cv.reshape(-1),\n",
    "                       'date': cv['date']})\n",
    "\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'est'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning N (no. of days to use as features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "129f1374e9e6404d96eaca20d7e719ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 44.00800196727117\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>N</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1.578091</td>\n",
       "      <td>0.792771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1.732682</td>\n",
       "      <td>0.897450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1.996401</td>\n",
       "      <td>1.026074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>2.070638</td>\n",
       "      <td>1.051505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2.139013</td>\n",
       "      <td>1.090372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>2.168969</td>\n",
       "      <td>1.111014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>2.221173</td>\n",
       "      <td>1.133356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>2.231244</td>\n",
       "      <td>1.161969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>2.261554</td>\n",
       "      <td>1.183697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>2.131510</td>\n",
       "      <td>1.109239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13</td>\n",
       "      <td>2.160399</td>\n",
       "      <td>1.141691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14</td>\n",
       "      <td>2.200309</td>\n",
       "      <td>1.162154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15</td>\n",
       "      <td>2.147358</td>\n",
       "      <td>1.112217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>16</td>\n",
       "      <td>2.141344</td>\n",
       "      <td>1.115083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>17</td>\n",
       "      <td>2.230971</td>\n",
       "      <td>1.201468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>2.201598</td>\n",
       "      <td>1.158560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>19</td>\n",
       "      <td>2.153076</td>\n",
       "      <td>1.102022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>20</td>\n",
       "      <td>2.213447</td>\n",
       "      <td>1.168192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>21</td>\n",
       "      <td>2.221052</td>\n",
       "      <td>1.126122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>22</td>\n",
       "      <td>2.240187</td>\n",
       "      <td>1.207940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>23</td>\n",
       "      <td>2.280890</td>\n",
       "      <td>1.206885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24</td>\n",
       "      <td>2.250376</td>\n",
       "      <td>1.210427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25</td>\n",
       "      <td>2.175764</td>\n",
       "      <td>1.163656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26</td>\n",
       "      <td>2.262333</td>\n",
       "      <td>1.200975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>27</td>\n",
       "      <td>2.232988</td>\n",
       "      <td>1.205607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>28</td>\n",
       "      <td>2.210988</td>\n",
       "      <td>1.192254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>29</td>\n",
       "      <td>2.277520</td>\n",
       "      <td>1.229877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>30</td>\n",
       "      <td>2.274386</td>\n",
       "      <td>1.186847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>31</td>\n",
       "      <td>2.181770</td>\n",
       "      <td>1.160796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32</td>\n",
       "      <td>2.160953</td>\n",
       "      <td>1.141405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>33</td>\n",
       "      <td>2.227846</td>\n",
       "      <td>1.205547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>34</td>\n",
       "      <td>2.290604</td>\n",
       "      <td>1.247211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>35</td>\n",
       "      <td>2.231764</td>\n",
       "      <td>1.148225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>36</td>\n",
       "      <td>2.356536</td>\n",
       "      <td>1.262810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>37</td>\n",
       "      <td>2.377870</td>\n",
       "      <td>1.284983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>38</td>\n",
       "      <td>2.329329</td>\n",
       "      <td>1.265976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>39</td>\n",
       "      <td>2.319928</td>\n",
       "      <td>1.191668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>40</td>\n",
       "      <td>2.307013</td>\n",
       "      <td>1.223027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>41</td>\n",
       "      <td>2.349869</td>\n",
       "      <td>1.239287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>42</td>\n",
       "      <td>2.346096</td>\n",
       "      <td>1.237368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>43</td>\n",
       "      <td>2.248891</td>\n",
       "      <td>1.193928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>44</td>\n",
       "      <td>2.250880</td>\n",
       "      <td>1.160349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>45</td>\n",
       "      <td>2.492610</td>\n",
       "      <td>1.306432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>46</td>\n",
       "      <td>2.377164</td>\n",
       "      <td>1.242541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>47</td>\n",
       "      <td>2.488249</td>\n",
       "      <td>1.287174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>48</td>\n",
       "      <td>2.345626</td>\n",
       "      <td>1.209795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>49</td>\n",
       "      <td>2.377607</td>\n",
       "      <td>1.285572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>50</td>\n",
       "      <td>2.324328</td>\n",
       "      <td>1.214136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>51</td>\n",
       "      <td>2.295092</td>\n",
       "      <td>1.201245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>52</td>\n",
       "      <td>2.372385</td>\n",
       "      <td>1.330567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>53</td>\n",
       "      <td>2.326885</td>\n",
       "      <td>1.276591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>54</td>\n",
       "      <td>2.401528</td>\n",
       "      <td>1.250337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>55</td>\n",
       "      <td>2.265865</td>\n",
       "      <td>1.185560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>56</td>\n",
       "      <td>2.296115</td>\n",
       "      <td>1.289104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>57</td>\n",
       "      <td>2.346476</td>\n",
       "      <td>1.267945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>58</td>\n",
       "      <td>2.368849</td>\n",
       "      <td>1.291484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>59</td>\n",
       "      <td>2.360460</td>\n",
       "      <td>1.280224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     N      rmse  mape_pct\n",
       "0    3  1.578091  0.792771\n",
       "1    4  1.732682  0.897450\n",
       "2    5  1.996401  1.026074\n",
       "3    6  2.070638  1.051505\n",
       "4    7  2.139013  1.090372\n",
       "5    8  2.168969  1.111014\n",
       "6    9  2.221173  1.133356\n",
       "7   10  2.231244  1.161969\n",
       "8   11  2.261554  1.183697\n",
       "9   12  2.131510  1.109239\n",
       "10  13  2.160399  1.141691\n",
       "11  14  2.200309  1.162154\n",
       "12  15  2.147358  1.112217\n",
       "13  16  2.141344  1.115083\n",
       "14  17  2.230971  1.201468\n",
       "15  18  2.201598  1.158560\n",
       "16  19  2.153076  1.102022\n",
       "17  20  2.213447  1.168192\n",
       "18  21  2.221052  1.126122\n",
       "19  22  2.240187  1.207940\n",
       "20  23  2.280890  1.206885\n",
       "21  24  2.250376  1.210427\n",
       "22  25  2.175764  1.163656\n",
       "23  26  2.262333  1.200975\n",
       "24  27  2.232988  1.205607\n",
       "25  28  2.210988  1.192254\n",
       "26  29  2.277520  1.229877\n",
       "27  30  2.274386  1.186847\n",
       "28  31  2.181770  1.160796\n",
       "29  32  2.160953  1.141405\n",
       "30  33  2.227846  1.205547\n",
       "31  34  2.290604  1.247211\n",
       "32  35  2.231764  1.148225\n",
       "33  36  2.356536  1.262810\n",
       "34  37  2.377870  1.284983\n",
       "35  38  2.329329  1.265976\n",
       "36  39  2.319928  1.191668\n",
       "37  40  2.307013  1.223027\n",
       "38  41  2.349869  1.239287\n",
       "39  42  2.346096  1.237368\n",
       "40  43  2.248891  1.193928\n",
       "41  44  2.250880  1.160349\n",
       "42  45  2.492610  1.306432\n",
       "43  46  2.377164  1.242541\n",
       "44  47  2.488249  1.287174\n",
       "45  48  2.345626  1.209795\n",
       "46  49  2.377607  1.285572\n",
       "47  50  2.324328  1.214136\n",
       "48  51  2.295092  1.201245\n",
       "49  52  2.372385  1.330567\n",
       "50  53  2.326885  1.276591\n",
       "51  54  2.401528  1.250337\n",
       "52  55  2.265865  1.185560\n",
       "53  56  2.296115  1.289104\n",
       "54  57  2.346476  1.267945\n",
       "55  58  2.368849  1.291484\n",
       "56  59  2.360460  1.280224"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'N'\n",
    "param_list = range(3, 60)\n",
    "\n",
    "error_rate = {param_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    # Split train into x and y\n",
    "    x_train_scaled, y_train_scaled = get_x_y(train_scaled, param, param)\n",
    "\n",
    "    # Split cv into x and y\n",
    "    x_cv_scaled, y_cv, mu_cv_list, std_cv_list = get_x_scaled_y(np.array(train_cv['adj_close']).reshape(-1,1), param, num_train)\n",
    "    \n",
    "    # Train, predict and eval model\n",
    "    rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                          y_train_scaled, \\\n",
    "                                          x_cv_scaled, \\\n",
    "                                          y_cv, \\\n",
    "                                          mu_cv_list, \\\n",
    "                                          std_cv_list, \\\n",
    "                                          lstm_units=lstm_units, \\\n",
    "                                          dropout_prob=dropout_prob, \\\n",
    "                                          optimizer=optimizer, \\\n",
    "                                          epochs=epochs, \\\n",
    "                                          batch_size=batch_size)\n",
    "    \n",
    "    # Collect results\n",
    "    error_rate[param_label].append(param)\n",
    "    error_rate['rmse'].append(rmse)\n",
    "    error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE/MAPE(%)')"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RZbVmjU7d37JFaxaVKRPoFuWf+2Dkv/7SMS4VKmjV/fLlgSuu0Ev58volcNtt\nQLdu1bF8eeCC1XPndAC4ayFqEc049OihX1K5tWnRIq1b1qaN/9objAYO1JIgn32mPzysCKeyHVbl\n1i0diM/+q6/qeqTLl2eW8HjgAeCDD/RH5IIFOoM1K4cj9H9YeoqZNcq3YOxSotxNmgQ8/7xWpL/v\nPg1smjUDZs70/bH8/flo2VIDHxEdx9Wxo872O3YMWL1au8uee06rt//1FzBrVvWAjt0ZP16Ls06c\nqGPWOnbUv0lqqgZvOUlP1+CkUycgOtp/7Q1G7dt7PtEgKQkoVkxfV1D4o1vaE7GxOkv1zz91ck2L\nFhqotW2bWWqEFIM1yrdg61Ki3E2bBjzxhE6BX7JEB+zOm6fPDRgAPPKI93WrsuPvz8djj2lg1rcv\nsGuXDk5eulS/FH75RbsNz57VbseICOCKK1ICNnYnKUmLyPbvr4OnXV57TQt+Op0atGVn7Vrg4MGC\n3QXqEhEBPPSQfuHv2mXtNdu26dqkhfgtGDAOR+YPxI4ddRWOIUO0GzQmJrBtCzpWVnsPlUuTJk28\nXfieLHA6nTk+Fx8vUr68yEMP6XV8vP/aFY5yO9f58eGHIsaI1K4tsmLFpc+tWiVy880igMgNN4j8\n9pvvjhsfL1K2rEjXrvZ+PubN0/a3aJF53OyO53r8X//S7adO9f/nNj1d5JZbRMqUEfnjj8ufX7JE\n2zZiRPavf/ppkchIkWPHbG2mz9j1mXY5cEAkIkLkP/+xtv1VV4ncdZetTQoYu8+1r1Wvrp/1++8P\ndEs844vzDGCdWIhvbPtNYYypZoxxGmOSjDFbjTFDstnmLmPMpozLGmNMA7fn9hpjNhtjEo0x6+xq\nJ/lGjRpAVJR2Md12W8EbTxAKFizQzJnDoUVK27e/9Pm2bYGvv9Ys25YtQO3awOuvX7qNt92XDodm\nihYv1i47u7o3XnpJ1wp0/VrPaTCya+zOuHFAdHQafvrJ/4OWZ8zQ8z1hgo6ry+rWWzXj9tJL+vfK\natEiIC4uPMYa+sKsWdrt7T7RIOvn1dUlf/q01u6qU4dDNgLN9fd49lktdlvQZnlaZiWi8+YCoBKA\nxhm3SwBIBlA3yzYtAJTJuN0JwI9uz+0FUN6TYzKzZq+cfkUsXy5SooRmbACRYsWYWcsvX/8yXrpU\nJCpKM06nTuW9/bZtItWq6d9z4EDNAuWUpbJi9mzd19VX63XJkiJffOH5fnKzZo3u+5lnPHtd164H\nJDpa5MgR37YnN3/+KVKunEjLliJpaTlvd/SoyJVXijRoIJKamvl4UpK+1zfftL+tvmJ3tic+Xj9X\ngMgnn2T/eXU9NnWqbjdqVHgrX4CFAAAgAElEQVT2BIRKZi3r3yg//8cEQlhk1kTkoIhsyLh9CkAS\ngCpZtlkjIscz7v4AoKpd7SHfS08HXnxRxxqcPauZgptuAipVKph1cIJF1gH9rqWIKlTQKfLFi+e9\njzp1NLt2003AO+/ojC1PCrW6czozF93+9lsdQHzmjA6Mf+454OJFz/aXnfR0HatWubL+QvfEbbcd\nQEqKjt3zl+HDdZmoqVNzHzNVtqxu8/PPwMsvZz7+2Wd63a2bve0MJQ6HZo8LFdIq9127anZy6VKd\nLdqvn44FrFwZ+Ne/9DVvvBEeJWtCld1Fk8OJX0p3GGOqA2gE4MdcNnsAgPvCEwJgpTFGALwjItnO\n8zHGPAzgYQCoWLEiEhISfNBiys7p06f/Pr+nTkVi/Pg6+OGHcqhV6xTuv38PqlU7jrp1q+Hbb2ti\nxIitmDMnGsb8FthGhyj3c+2pqKjS6NGjLkaO3IaoqHQMG9YAaWmFMGjQFmzceNSjfY0eDTz2WCNs\n2lQKbdsegjHb4WmzZs68GmlpV6F16yPYvTsJV18NjBtXBtOn18D48SWxdOkJPP/8NpQv7/2shqVL\nr8T69bXx3HPbsG7dYY9eW6HCadSvfwKvv14EjRv/aHtx2Z9/LoXp0xuhb99fceTInjzPZ6lSQJs2\ndTBmzBWoUmU9atY8g5kzG+G66wx2797gVeX+QMjPZ9qqyEigUaP6WL++LAD9ARkdnYaiRdNQrNhF\nFCumtytVKowDB4rh1lv3wpi9Hn+mg50/zrUvNGum1+5NNUYfD4Hm+/c8W0m/5ecCoDiA9QB65rKN\nA5p5K+f2WOWM6woAfgZwc17HYjeo702YkJmSdqV833lHB0VHRWk3THp65vbbtmn3wpQp/m9rOMlv\nej0+XqR0aZHChXXQ9fz53u+nfHnt5o6MFPnqK8/3MXGifiY2bLj8uZkztds8Jka3y3rsCRPy3v/x\n4yJXXKFdiu6fRaucTqfMnattXLzY89d7IjVVpE4dHVB95oz11x05oueoVi2Rffu0rS++aP0cBQN/\ndM25Pq9Dh2o386pVOW8zYkRodbl5IlS6QUOdP7tB7Q7UogCsADAsl23qA9gN4NpcthkF4Km8jsdg\nzffcxxA4nU7597/1U1O+vMj331++fXq6SM2aIp07+7+tdnAPVl388QWZn/8E1q8Xue02/TsBIkOG\neLcf97/9u+9mjjXz5Mvt/HmRqlVFHI6ct9m2LXM2WP/+IhcvejZ2ZehQHS+ZXTBohdPplPPnRSpX\nFunQwbt95Mb9MzRunL7P8eM9/wyNGqWvrVlTr997L7SCDX+MWctr/FOoj5GyisGaf4RFsAbAAJgJ\nYFIu21wFYBeAFlkejwFQwu32GgAd8zomgzV7xMfrr9Ratf4SQKRRo+xLDbgMHSpSpIjI6dP+a6Nd\nXP+ZL1iggYe//nP35j+BH38U6dJF/1XHxGjG6qmnvG+ve5Bx/rzIP/4hUqOGyMsvW9/HrFnaniVL\nct/u9GkNlFyTEMqUsdbmrVs14/fww9bblJXrXI8ercdPTvZ+X9lxfWY+/FAkOlrkppu8/5vExWkb\ny5QJvSDD7gDCyg+rQP348jcGa/4RLsFaK+i4s00AEjMunQEMAjAoY5v/ATju9vy6jMevyej6/BnA\nVgDPWTkmgzXP5PYf1/HjIosWiTz+uMj112dmaVq0ELlwIff9fvmlbrtokX1t96dPP9XMTcWK1oOI\n/MrpP4Hs/maTJ4tce62e87JltVZRuXK+zx64ZnTOnm1t+/R0kYYNtaZbbjMe3XXrpscwRuTRR3XW\nZG77b9tWu3sPH7a2/+y4zvXBg9q1P3So9/vKyVdf6b4LF9a/kbd/i8OHRYoXl1zrrwUrBhD+w3Pt\nH+EyG3S1iBgRqS8iDTMuy0RkqohMzdjmQREp4/Z804zH94hIg4zLP0VknF3tLMjcK8ufOaOzCG+9\nVWfFlSunMwjffVeXJIqJAbp0+R3JyTqjLzc33QSULKnV8cPBtm0aqv7xh56nI0cC1xb3v9k33+hi\nx48/Dhw6pLMF9+7Vquzz5vl+htWdd+p6fS+8AFy4kPf2TieQmAg8+aS1KvFOp65XOmyYfubeekvr\nsb32WvYrKnz2mVasHzNG1/vMryuvBHr10jpdZ87kf3/uTp7Uc3b+vK4Q4e3swy1btE7d888jYKsu\nEFEAWInoQuXCzJrn4uP1l3qhQvprvVAhkVatREaOFPn6a62h5j5mzWqWpndvrQ9lNaMSrFJTNUsV\nFaVZxshIPU+PPCKSkuLbY2U3mcOV6fzrL5FvvhGZNEmkfXudNODKQA0e7L8u588+0+O++27e23bu\nLFKhgsi5c3lvm91YojJlRGJj9Xj/+IeOZ3NNcDh7Vse5Va8u8tJL3r8fkUt/Ha9ercd755387dPd\nuXP6byEiQuS557zPcob6eCtme/yH59o/wqIbNBAXBmue27AhM1Dr1+/yL/3cAojczJyp+/zpJ9+3\n2Z+efVb+HhAuIrJypUjRovpYkyYiu3f77liuL9+lS0VeeWWjPPywjv2rWjWz4DAgUqmSBi+ALjnk\nT+npIs2ba5tyC8Jcs4JHj7a239y65L/4QqRuXd1fVJQGUi++qPdLlcp/sOL+H66r67ZePe9mlmbn\n/vu1ra+8ove9DbJCfbwVAwj/4bn2DwZrDNb84uRJnQFXqJDIsGF5f4F48sE8ckT3+8IL+W9nIFWr\npoGJe4YwPl7knnt0rFSpUprx8dWX6BdfZGbNAB0n16OHBidLl4r8/nvgSw+4xiROmpTzNg8+qIPp\n8zOWzN2FC1oOxlWhvlAhHf/li/ee9XPtmvn69df53/dvv2mAedNNlz4eSkGWrzCA8B+ea/9gsMZg\nzXbp6VpOwf1LN69f/J5+MFu10pmjvuavDMP330uuS/r88ktmN13RopkLo+ene2r48MxA7cknL38+\nWLrCWrfW2mbZLV116JBmBAcO9P1xjx8XufFGPT+PP+6bfWb9XJ85o4F4797533e/fnou9uzJ/75C\nHQMI/+G59o+wmGBAwe3ddzOXARoyRB/z9VIfXbroAtQHDvhmfy6uQfZffqn3nU69Hxvr2+O88YZW\njx8wIPvnq1cHVq/WAf7nzunkjKFDvV+Waf164L//1QHk/fvvxYwZlw8gD5blWcaNA/78U89RVlOm\nAKmpwBNP+P64GzcCO3cCI0YAs2fbM8C+WDH9d/Hpp/n77H73nbbx3/8GatTwXfuIqACyEtGFyoWZ\nNWt+/lm7qNq392wCgKe/IrZu1QzI1Kmetc8KV4HQbt3sySz99pt2Rw4bZm37+fO1Ww7Q7Jinzp/X\nYqfG6CB+TyZzBErXrtoNfOxY5mNnzuiEjG7dfH88u7KK2X2ud+3Sv4W33fgXL4o0bqxd6OFQb9AX\nmO3xH55r/2BmjWxz+rRmfsqUAT780FpJBW/VqQNccw2weLFv9/v558DYsdr2zz8Hbr/d9wsxT5mi\nnZGPPmpt+7JlgaJFdV27SZOAr77y7HivvALs3g2MHJm5OHewL2o8dqyWpPjvfzMfmzkTOHpUy3X4\nmj+zijVr6kLz06ZlXzYkL9OnAxs2aDmcmBjft4+IChYGawWICDB4sHYjzZ4NVKhg7/GM0a7Qr74C\nzp71zT6XLdPgrGZN7aIsVky7dJcv983+Ae3SnDYN6N7dWveVqxt24ULg1Vf1y71bN+tddDt26ILp\nvXppsObO4QCGD/f8PfhD/fpAnz7aFXroEJCeDrz+OtC0qdba87Xhwy8Pyu08PxUr6vv69NPMx5xO\nDcByc+IE8OyzQKtWen6IiPKLwVoB8sEHwKxZGhDExfnnmF27AikpnmeasrNqFdCzJ3D11VqYdsEC\nvaSlAT16+G780kcfaXbINZYvL+4Zn6FDgbvv1uB05sy8X5ueDjz0kGbl3nwzf+0OhKpV9e/70kta\nBDk5GejY8dJsW6i66y7N3o4dq/etjo0cPVo/n5Mn6w8WIqL8YrBWQGzdqpXTW7cGnnvOf8e9+Wag\nRIn8d4UmJGim69prgX79Miv0d+yomZWUFOC99/LfXhHtxmzQQNtuhXvGxxjNyjVurBmZHTtyf+20\naboixKuvagX9UNO5M1C4sHYbjxih2dqpU30/2SMQ2rQBBg7Ufzu33mpt4khSkgbdDz4INGrkv7YS\nUXhjsBamJk7MzDSdOaNfNNHRQIsWQESE/9pRuDDQoYNmXUS828fq1dqdWqOGzgAdPfrSL8yxY4Hm\nzfUYv/ySv/bGx+uX85Ah3mdFihbVLtEiRXTJrr/+yn67/fs10GvTBrjvPu/bHEgOh47PungR2LRJ\nP2vezIQNVi+9BFSpot3vgP4tc/oci2hmNSZGZ8sSEfkKg7Uw5b6G5GOP6fqW6emaWfO3rl2Bgwd1\nwLWnfvxRszdVqmhXanbj7KKigI8/1tt9+1pbtzInb7yh60z27ev9PgDgqqs0aNm5E+jfX8+9OxHg\nX//SIGfatNDuLuvTB7jxRr2dn3Uvg9GGDVqGpFcv4PhxDb5btcp+fdzFi4GVK/XHhC/WKiUicmGw\nFqYcDuCTTzRQmj5dB+IvXBiYL9LOnTUYsdIV6p4RXL9es3IlSuikgty6CWvU0IkGP/6oi1x7Y9cu\nzc4NGqRZyPyKi9MB959/rouNu5s7V8/Hiy/qjNlQ5nRqUPqf/wDvvx8+i4u7xqjNnQvMn6+TWIoX\n167tm2/WbO8TT+h2qam6AH3dunrJaxICEZEnGKyFqVOnNGNz5ozef+KJwGU8ypfXzMuSJXlv68oI\n/u9/QLt2GjSlpABt2+b92jvu0DFGEycCK1Z43s7/+z8gMlJnzPrKo49qUd3Ro4HPPtPHjh7VbGds\nrPVJDMHKPaB5+WW9dmV0Q13WUiFt22rgPXSovtfvvtPxjZ066aSS3buBe+/VMZXhMGaPiIIHg7Uw\ntHUr0KyZftHExGim6Z13AvsF2rWrZsp+/z337RwObe/DD+sszwsXNKthNdB8/XXg+uu16/HgQevt\n++svzUD27g1UqmT9dXkxRgfcV62q3YVJSZqBOX5c3+Nrr/nuWIEQLCsq2CGnUiHPP69ZxD17dJv0\ndP2MXnut/lAIpzF7RBQcGKyFmdmzNVA7dAgoWTKzqy3QGY+uXfU6t+xaWppOFhg2DChdWgMoT8dA\nFS2q3b/Hj+sMPvexYrnVyJo+XbORQ4daP5ZV0dE62zM1FWjZUkt69OkDPPNM6Gdg/F37LJiUKQNM\nmKCTWuLitGzJ4MEM1IjI9xishYnUVA1s7roLaNJEb7uPUQt0xqNuXV1LM6dg7dAhHZ82YoS2tVAh\nvf32254HmHXratC1caNmr4Dsa2S5xselpWkXaIsWGrDZMd6od2/N+h0/DpQrp+OfmIEJD8nJwJYt\n3n9eiYjyEhnoBpDnJk7UoMP1Rb9vnwY6O3YATz0FjB+vMySzcjgCFxwYo9m1//1PVwgoWjTzuVWr\ndMzPqVO66PX06Zl11BwO7xZGnzhRu13fe08nHezZo+emXLnM47vGxw0ZouON+vXLPJYdhgwBNm/W\nNrmCUgpt7mP28vN5JSLKDTNrIci9LMcXXwD16umv+9GjtXJ8doFaMOjaVQMl12oGFy9qgd4OHbTU\nwdq1OhnBF2OgjAEWLdIs25YtuqLAmDFa7DYmRmePTpyos/pGj9YZp2+/be+XrNOpkwyYgQkf4Txm\nj4iCBzNrIcj1hdCtmy7MHhEBzJihg+qD1cSJGigVL65doQ0aaEmPLVu02vsbb2h5kX/+8/LXepsR\nXL8eOHw4Mzh66SUNynbsALZv18uOHRo0usar2RmoMQMTfrIbmxfIDDYRhScGayHq+ut1wXBAuw6D\nOVADMrOBjRppF+fs2RogPfusPdXecwuO7rwzc7uvvtL7gwdrQGfXF21uGRh+sRMRUW7YDRqiHnhA\ng7XBg3UcWLB3qbmCk8RE4NixzIXO7VqWx0r3lNOpszLnzbN/xmxBnjVJRET5w2AtBL33npbk6NFD\nF9AOdFkOqxwO4PHH9fbw4fZmA60ERxxvREREoYDBWogR0erpxYvr8kpA6AQZTqcW5x0xQtse6OCS\n2S4iIgoFHLMWYpYs0TUs33hDy1C4BPugZg6wJyIi8g4zayHk/HmtFXbddb5dv9If2OVIRETkHWbW\nQsiUKVpPbenS4K2llhOWOCAiIvIOM2sh4sgRLd7avj3QqVOgW0NERET+wmAtRIwapQubv/aaVucn\nIiKigoHBWgjYtg2YOhUYNCj7Cv9EREQUvhishYAnn9RSHaNHB7olRERE5G+cYBDkvvgCWL5cuz/L\nlw90a4iIiMjfmFkLYhcuAMOGAbVqAY88EujWEBERUSAwWAsyEydmVvafOhXYvh245x5g0qTAtouI\niIgCg8FakImN1cr+n32mM0AbNdLVCmJjA90yIiIiCgSOWQsyrsr+t94KnDuna4EuWMDisURERAUV\nM2tBqGZNIDVVbz/6KAM1IiKigozBWhAaNAhITweGDAHefjtzDBsREREVPAzWgsyMGVquo1cvnVQw\nd66OYWPARkREVDAxWAsyr70GREfrou1A5hi2tWsD2y4iIiIKDE4wCCIbNgCbNgEjRgAVKmQ+7nBw\n3BoREVFBxcxaEHn+eaBMGV1eioiIiAhgZi1ofPutjlWbMAEoVSrQrSEiIqJgwcxaEBABnn0WqFRJ\nS3UQERERuTCzFgSWLwdWr9ZJBcWKBbo1REREFEyYWQuw9HTgueeAGjWABx4IdGuIiIgo2DCzFmDz\n5wMbNwIffggULhzo1hAREVGwYWYtgC5e1DId//wn0LdvoFtDREREwYiZtQCaORNITgYWLgQiIgLd\nGiIiIgpGtmXWjDHVjDFOY0ySMWarMWZINtsYY8xkY8wuY8wmY0xjt+cGGGN2ZlwG2NXOQElJAUaN\nApo1A7p3D3RriIiIKFjZ2Q16EcCTIlIHwA0AHjHG1M2yTScAtTIuDwN4GwCMMWUBjATQHEAzACON\nMWVsbKtfTJyYucbnO+8Av/2m637+97+BbRcREREFL9uCNRE5KCIbMm6fApAEoEqWzboDmCnqBwCl\njTGVAHQAsEpEjonIcQCrAHS0q63+EhurwdmyZcC4cUCjRsDLL+vjRERERNnxy5g1Y0x1AI0A/Jjl\nqSoAfnO7vz/jsZwez27fD0OzcqhYsSISEhJ80WRbGAM8+2xp9OpVDykpETh37gLGjNkKY04giJv9\nt9OnTwf1+Q0nPNf+w3PtHzzP/sNz7R/+PM+2B2vGmOIAFgAYKiJ/ZX06m5dILo9f/qDINADTAKBp\n06YSFxfnfWP9IC4OeO01YP9+4IknovDEEw0D3STLEhISEOznN1zwXPsPz7V/8Dz7D8+1f/jzPNta\nusMYEwUN1D4SkU+z2WQ/gGpu96sC+D2Xx0Oe0wkcOKBdoG+/nTmGjYiIiCg7ds4GNQDeA5AkIq/l\nsNnnAO7JmBV6A4CTInIQwAoA7Y0xZTImFrTPeCykOZ3AHXfoWqB9+wJz5+oYNgZsRERElBM7M2st\nAfQH0NoYk5hx6WyMGWSMGZSxzTIAewDsAvAugH8BgIgcA/AigLUZlzEZj4W0tWuBF1/U29deCzgc\nGrCtXRvYdhEREVHwsm3MmoisRvZjz9y3EQCP5PDc+wDet6FpATN8OPDRR3r72mv12uHQCxEREVF2\nuNyUnyUnA4UKAddcE+iWEBERUShgsOZnyclA9epAkSKBbgkRERGFAgZrfpacDNSqFehWEBERUahg\nsOZHIsDOnZnj1YiIiIjywmDNj/74Azh1isEaERERWcdgzY+Sk/WawRoRERFZxWDNjxisERERkacY\nrPlRcjJQuDBQrVre2xIREREBDNb8audO4B//ACIiAt0SIiIiChUM1vwoOZldoEREROQZBmt+kpYG\n7NrFYI2IiIg8w2DNT379FTh/nsEaEREReYbBmp+4ZoJy9QIiIiLyBIM1P9m5U6+ZWSMiIiJPMFjz\nk+RkoEQJoGLFQLeEiIiIQgmDNT9xzQQ1JtAtISIiolDCYM1PWLaDiIiIvMFgzQ9SU4F9+zi5gIiI\niDzHYM0P9uwB0tOZWSMiIiLPMVjzAy7gTkRERN5isOYHrLFGRERE3mKw5gfJyUCFCkDp0oFuCRER\nEYUaBmt+wJmgRERE5C0Ga36wcye7QImIiMg7DNZsduoUcPAgM2tERETkHQZrNuOaoERERJQfDNZs\nxrIdRERElB8M1myWnKzrgdasGeiWEBERUShisGaznTuBatWAokUD3RIiIiIKRQzWbMayHURERJQf\nDNZsJMJgjYiIiPKHwZqNjhwBTpxgsEZERETeY7BmI84EJSIiovxisGYjV401rl5ARERE3mKwZqPk\nZCAyEqhePdAtISIiolDFYM1GyclaXy0yMtAtISIiolDFYM1GnAlKRERE+cVgzSbp6TpmjcEaERER\n5QeDNZscOACkpHByAREREeUPgzWbsGwHERER+QKDNZswWCMiIiJfYLBmk+RkoFgxoHLlQLeEiIiI\nQpnlohLGmAoAWgKoDOAcgC0A1olIuk1tC2mumaDGBLolREREFMryDNaMMQ4ATwMoC2AjgMMAogHc\nBqCmMWY+gFdF5C87Gxpqdu4EGjYMdCuIiIgo1FnJrHUG8JCI/Jr1CWNMJIAuANoBWODjtoWsCxeA\nPXuA3r0D3RIiIiIKdXkGayLy71yeuwhgkU9bFAZ++QVIS+PkAiIiIso/jycYGGNuMMbEG2O+M8b0\nsKNRoY4zQYmIiMhXrIxZu1JEDrk9NAxANwAGwBoAC21qW8hisEZERES+YmXM2lRjzHoA/xWRFAAn\nAPQDkA6AkwqysXMnULasXoiIiIjyI89uUBG5DUAigCXGmP4AhkIDtWLQGaHZMsa8b4w5bIzZksPz\n/zbGJGZcthhj0owxZTOe22uM2Zzx3Dpv3lggcQF3IiIi8hVLY9ZEZDGADgBKA/gUwA4RmSwif+by\nsg8AdMxln/8VkYYi0hDAMwC+FpFjbps4Mp5vaqWNwYTBGhEREflKnsGaMaabMWY1gHhoIdw+AHoY\nYz42xtTM6XUi8g2AYzk9n0VfAB9b3DaonTkD7N/PYI2IiIh8w4hI7hsYswnAjQCKAlgmIs0yHq8F\n4EUR6ZPLa6sDWCIi1+eyTTEA+wH8w5VZM8b8AuA4AAHwjohMy+X1DwN4GAAqVqzYZM6cObm+H7vt\n2hWDhx6KxciRWxEXl1viMfScPn0axYsXD3QzCgSea//hufYPnmf/4bn2D1+cZ4fDsd5KD6KVCQYn\nodm0otDVCwAAIrIz4/H86grguyxdoC1F5PeMJa5WGWO2Z2TqLpMRyE0DgKZNm0pcXJwPmuS5iROB\n2FigdGm936PHP3HsGLB2LTB8eECa5HMJCQkI1PktaHiu/Yfn2j94nv2H59o//HmerYxZ6wGdTHAR\nOgvU1/ogSxeoiPyecX0YWhqkmQ3H9anYWF2xYPlyvX/ggN6PjQ1su4iIiCi0WcmspYjI/+W2gTGm\nuIic9vTgxphSAG4BcLfbYzEAConIqYzb7QGM8XTf/uZwAHPnAp06ASVKAAMG6H2HI9AtIyIiolBm\nJVj7zBiTCOAzAOtF5AwAGGOuAeAA0BvAuwDmu7/IGPMxgDgA5Y0x+wGMBBAFACIyNWOzHgBWuvaZ\noSKAhcYYV/tmi8hyr96dnzkcQJkywKFDwNChDNSIiIgo/6ysDdrGGNMZwEAALY0xZaBdojsALAUw\nIMsKB67X9bWw7w+gJT7cH9sDoIGVxgebxYs1ULv5ZuDttzVYY8BGRERE+WElswYRWQZgmc1tCWlO\nJ3B3RmfumDFAerqOWWNXKBEREeWHlTpr7uPJWmZ57lE7GhWK1q4F2rcHihQBmjfPHMO2dm2gW0ZE\nREShzMps0GFut7NONLjfh20JacOHA7t3Ay1aANHR+pjDET5lO4iIiCgwrARrJofb2d0vsI4fBxIT\nAZa2ISIiIl+yEqxJDrezu19gffMNIMLxaURERORbViYY1M5YcsoAqJlxGxn3r7GtZSEmIUG7P5sF\nffleIiIiCiVWgrU6trciDDidQMuWOsGAiIiIyFfy7AYVkX0AGgG4A0BtEdnnfrG9hSHg2DFg0yaO\nVyMiIiLfs1K6YwqAJwCUA/CiMWaE7a0KMV9/zfFqREREZA8r3aA3A2ggImnGmGIAvgXwor3NCi0J\nCUCxYly0nYiIiHzPymzQ8yKSBgAichYs13EZ13i1woUD3RIiIiIKN57MBgUunRFqAIiI1LetdSHg\nyBFg82agT59At4SIiIjCEWeD5tPXX+s1x6sRERGRHfIM1nKa8ZmxTmg/AI/4ulGhJCEBiIkBmjYN\ndEuIiIgoHFnJrP3NGNMQGqD1BvALgE/taFQocTqBVq2AqKhAt4SIiIjCUZ7BmjHmWgB9APQFcBTA\nJwCMiBT4jr/Dh4GtW4G77w50S4iIiChcWcmsbYeW6+gqIrsAwBjzhK2tChEcr0ZERER2s1K6oxeA\nQwCcxph3jTFtwPIdAHS8WvHiQOPGgW4JERERhSsry00tFJE7AdQGkABdzaCiMeZtY0x7m9sX1Dhe\njYiIiOxmJbMGABCRMyLykYh0AVAVQCKAp21rWZD74w8gKYldoERERGQvKxMMyubw1LyMS4GUkKDX\nXLydiIiI7GRlgsERAPsBXMy47z5eTQBc4+tGhYKEBKBECY5XIyIiIntZCdb+D0AcgO8AfAxgtYiI\nnY0KBU4ncNNNQKRHleqIiIiIPGNlgsEQAA2hXZ79AWw0xkw0xtSwu3HB6uBBYMcOjlcjIiIi+1ma\nYCDKCWA4gKkA7gPQ1s6GBTOOVyMiIiJ/sTLBIAZAdwB3ArgCusRUYxH5zea2Ba2EBKBkSaBRo0C3\nhIiIiMKdlRFXhwHshI5X2wWdVBBrjIkFABEpcOuDOp3AzTcDERGBbgkRERGFOyvB2jxogFY74+JO\nUMAWcz9wANi5Exg0KD63tMcAACAASURBVNAtISIiooLASrD2DoAfOANUcbwaERER+ZOVCQYDAKw3\nxswxxtxrjLnS7kYFM6cTKF0aaNAg0C0hIiKigiDPzJqIDAIAY0xtAJ0AfGCMKQXACWA5gO9EJM3W\nVgaRhASOVyMiIiL/8WRt0O0i8rqIdATQGsBqAHcA+NGuxgWLiRM1o/bbb8Du3VpfzenUx4mIiIjs\nlGewZoxp7Xa7BgCIyDkRWQbAKSJNbWxfUIiNBXr3BqZM0fsxMXo/Njaw7SIiIqLwZyWz9orb7QVZ\nnnveh20JWg4HMHcu8PrrQHQ08Oyzep8rGBAREZHdrARrJofb2d0PWw4HUKwYkJICDB7MQI2IiIj8\nw0qwJjnczu5+2HI6gUKFgMceA95+W+8TERER2c1KnbVrjDGfQ7NortvIuF8gFnN3OnWM2rx5mlHr\n0UPvsyuUiIiI7GYlWOvudvuVLM9lvR+W1q69NDBzjWFbu5bBGhEREdnLSp21r93vG2OiAFwP4ICI\nHLarYcFk+PDLH3M4GKgRERGR/ayU7phqjPlnxu1SAH4GMBPARmNMX5vbR0RERFSgWZlgcJOIbM24\nfR+AZBGpB6AJgGxyTkRERETkK1aCtfNut9sBWAQAInLIlhYRERER0d+sBGsnjDFdjDGNALSErgcK\nY0wkgKJ2No6IiIiooLMyG3QggMkArgQw1C2j1gbAUrsaRkRERETWZoMmA+iYzeMrAKywo1FERERE\npPIM1owxk3N7XkQe911ziIiIiMidlW7QQQC2AJgL4HcUoPVAiYiIiALNSrBWCcAdAO4EcBHAJwAW\niMhxOxtGRERERBZmg4rIURGZKiIOAPcCKA1gqzGmv92NIyIiIirorJTuAAAYYxoDGArgbgBfAFif\nx/bvG2MOG2O25PB8nDHmpDEmMePygttzHY0xO4wxu4wxT1ttIxEREVG4sTLBYDSALgCSAMwB8IyI\nXLSw7w8AvAldmion34pIlyzHiwDwFrQA734Aa40xn4vINgvHJCIiIgorVsasjQCwB0CDjMt4Ywyg\nEw1EROpn9yIR+cYYU92LNjUDsEtE9gCAMWYOgO4AGKwRERFRgWMlWKth4/FvNMb8DJ1l+lTGGqRV\nAPzmts1+AM1tbAMRERFR0LJSFHdfdo9ndFf2AZDt8xZsAHC1iJw2xnSGrjlaC9mXBpGcdmKMeRjA\nwwBQsWJFJCQkeNkcysvp06d5fv2E59p/eK79g+fZf3iu/cOf59nKmLWSAB6BZrw+B7AKwKMAngKQ\nCOAjbw4sIn+53V5mjJlijCkPzaRVc9u0KjTzltN+pgGYBgBNmzaVuLg4b5pDFiQkJIDn1z94rv2H\n59o/eJ79h+faP/x5nq10g34I4DiA7wE8CODfAAoD6C4iid4e2BhzJYA/RESMMc2gM1OPAjgBoJYx\npgaAA9DsXT9vj0NEREQUyqwEa9eISD0AMMb8D8ARAFeJyKncXmSM+RhAHIDyxpj9AEYCiAIAEZkK\n4HYAg40xFwGcA9BHRATARWPMo9B1RyMAvJ8xlo2IiIiowLESrF1w3RCRNGPML3kFahnb9s3j+Teh\npT2ye24ZgGUW2kZEREQU1qwEaw2MMa7xZQZA0Yz7rtIdJW1rHREREVEBZ2U2aIQ/GkJEREREl7O8\n3BQRERER+R+DNSIiIqIgxmCNiIiIKIgxWCMiIiIKYgzWiIiIiIIYgzUiIiKiIMZgjYiIiCiIMVgj\nIiIiCmIM1oiIiIiCGIM1IiIioiDGYI2IiIgoiDFYIyIiIgpiDNaIiIiIghiDNSIiIqIgxmCNiIiI\nKIgxWCMiIiIKYgzWiIiIiIIYgzUiIiKiIMZgjYiIiCiIMVgjIiIiCmIM1oiIiIiCGIM1IiIioiDG\nYI2IiIgoiDFYIyIiIgpiDNaIiIiIghiDNSIiIqIgxmCNiIiIKIgxWCMiIiIKYgzWiIiIiIIYgzUi\nIiJSEycCTueljzmd+jgFDIM1IiIiUrGxQO/emQGb06n3Y2MD264CLjLQDSAiIqIg4XAAc+cCnTvr\n5Ztv9L7DEeiWFWjMrBEREVGmKlWAlBTg00+B229noBYEGKwRERFRptdf1+siRYB33wVWrAhse4jB\nGhEREWVwOoH33gOuuQZYsABISwN69Lh80gH5FYM1IiIil4I+G/KbbzRA69sXuPVW4PHHgXPngA8/\nDHTLCjQGa0RERC6u2ZDx8cDhwwVvNmStWkB6OtC1q96fMAGoXx9YsgQ4eDCwbSvAGKwRERG5uGZD\n3nYbULGidgEWpNmQixfr+3YFp9HRwMcfA6dPA/feq4Ec+R2DNSIiIncOB1C9ut4uXBho1SqgzfGb\nCxeAL77Q7s9CbuFB3bo66WDlyszJB6EmxLu3GawRERG5i48Htm4FSpUC/vxTx215w1cBgr8CjW+/\nBU6ezOwCdffww0DPnsAzzwDr1/v2uLnx1XsP8WK/DNaIiAqSEM8w2M7p1Npi6enAq68CN9wATJ2q\nXaGe8lWA4K9AY/FiLdfRrt3lzxmjZTwqVNDJB6dP+/bYOXG99xUrdKKDt+/d4dDu3K5dgUaNgO7d\n9f2ESPc2gzUiooLE9eW3eLF+4YZYhsF2a9cC/frp7XbtgNmzgagoYOxYz/flcAD/93+6EsCTT+p5\n9mb8m2scXc+ewHPPeb+f3IjoZ6J1ayAmJvttypYFZs0Cdu0Chgzx3bFz43rv3boBZcposPXhh56/\n92+/BYYNA86cARITgVOngF69gJYtgXHjgI0bdTJFkP6QYbBGRFSQNGumwUO3bkC5ckCXLiGVYbDd\n8OHAvn06K/Kqq4AaNYCRI4HNm4Hlyz3b14kTGuSlpACvvQYMHuz9eRbR/Y0fn7/95GT7dmD37uy7\nQN3Fxemx33//0myjnUFNzZrA+fNAaqoGWw89BEyfriVG8vLHH8CAAcDNN+vtkiU14C1dGrj7bh2n\n9/zzQOPG2v5OnYBRo7Q7OIh+yDBYIyIqCNLTNSNx3XXAzJk6aPz8eeDsWaB/f+A//9Evs4LuwgUg\nIeHSrsCnngKuvRZ49FENvKw4f14zN9u3A5GROmD/rbe8Ky4rAjz2mN6OiPB+P7lZvFiv8wrWAODp\np/U93X8/sHev/UHN6NF6/dhjOo4wJkaP3bAhsHSpnp+s0tKAN9/Uz/vHH2u2NC0NWLRIA+hPPwWW\nLdNs2qFDwIwZ+jePjNTjlS9vTwbTSwzWiIjC3Zo1OvbqnnuASpWAN97QGmIjRmjXUrNmwCuvaBZp\n6FDNPARpd5DtfvxRu4fbts18rEgRDZB279Yv97yIAA8+qBMVihXT4LhQIZ1V6j72zKqXXwa2bdOA\nIyICaN7cu/3kZvFiHctVtWre27Zrp8HN2bNAvXpa5sSuoCY+HvjgAw3MJk8GFi4Ejh4FXnhBA+cu\nXTTzNmVK5mt++AGoXVuDu9hYzYo2aADMm5fZRlf36tq1WqrknnuAOXM0ezlgAHDxoj0ZTG+JSNhc\nmjRpImQfp9MZ6CYUGDzX/hNW53rCBJH4+Mz7e/eKxMWJACKVK4vMmCHy5Zci5ctnbhcfr/dnzBAZ\nMEAkIkIkMlIkOvr/27v3OJvq/X/grw8zyF2UHLrplJKUQqWSKaSim9JVitMcktRBouiky7foqlAq\nTPVDc4gMcp2RUh1yciKnC5UMipLcx1zevz/ee5k9Y1/W3nuttS/zej4e85jZa6291md/9pq13utz\nFZk6tew2/vuOUNLk82OPiVSqJLJz55HrbrlFpGpVke+/D72PkSM1zzt1Ks2zzEyR9HSRadP0e7Kr\npES/u+OOEzl0SGTQIE3fm28G3U/Eeb1jh+5z5MjI3nfPPfo5AZG77xbZtSuy99vRt6/uPyurdFlu\nrn72ggKRV18VqVNHt7n0UpHevfXvSpVERozQ/IuEda6PGBH2nHfinAbwhdiIb+IeYDn5w2DNXUlz\nsU0BzGvvpFReWzeaefNEHnlEgwNApGdPkb17dZvyAZ31PuvGv3Gj3oQrV9b3Xn55zIGaSBLl80UX\nibRtG3jdli0itWqJXHFF8CDgrbc033r3LrvNpk36ffTtG1l63n9f9zdlir7+/XeRunVFrroq6Fsi\nzuusLD3GqlX232Oda8OGiVSvrsFRkyYiCxdGduxw+vQRqVmz9PwNZPduPccBEWNEjjpKZO7cyI9V\n/qEkzENKSgRrACYB2A5gXZD1twP4yvfzKYCz/db9BGAtgDV2P4gwWHNd0lxsUwDz2jspl9dLl2rJ\nGKClQNOnR7efn38W+etfdT8DBsScrKTI5z//1CD1kUeCb/Pyy5onM2YcuW7RIs37Tp20FKy8vn01\nYPvpJ3vpKS4WadFC5LTTRAoLS5ePHq1pcCqIuPFGkUaN9Hh2BApq6tYVOeEETVdmpgZQsdq7VwO1\n3r3tbT9okB5/xIjojhfuQaYcL4M1N9usTQHQJcT6HwFcKiItATwBYGK59Rkico6ItHYpfUREicHJ\nsc/q1NH2NoD2bLz55ujStGEDsHOntpGaONH5Bu2J6KOPtBG6f3u18u69V9tPDRyowz9YvvpKOxQ0\nbw7MmKHDfZQ3fLiOV/b00/bSk50NrFunDd7T0kqXDxigPVUfeij26Z8OHdIxzLp2LTtrQSirVpVt\no5aRoQ32MzOBIUO0d/EJJ+g4df4iPafff790mqtw8vK0Hd2IEcCECdGdrw89dGQbtYwMXR5vdiK6\naH8AnIQgJWvltqsHYIvf658ANIj0eCxZc1dSPBmnCOa1dxIiryOsfgnp5pu1dGHw4Oj34X/8wYN1\nf/XqpX6btfvv1yq0gwdDb9e/f2kei4hs3qz5Vbu2/h3Kvfdq6duPP4berrBQS9RatAhc4mVVXQYo\nOY0orxct0v3MmWP/PeGsWCHSuLHu99prtYQsmnP6sstETjklfLszJ/9/IpAqJWuR6APgQ7/XAmCR\nMWa1MSYzTmkiIvKG1TPt+uv1KT7aIQNyc7XHW9u2wJgxuo9oeg36l5wMHQrUqgW0aKHLnZZIMyos\nXqzjcVWtGnq77t11gvMXXgBWrAAuvVR7KD7/fPjelMOGaQnWU0+F3u7dd4HvvgNGjQpc4nX77drD\ncdgwHX8sWjk5+lkuvzz6fZTXrp2mvXt34IMPgJYtIz+nf/pJz+e77tLSyFAClfRZPT1ThNHAzqWd\nG3MSgLki0iLENhkAxgO4WER+9y37i4hsNcYcC2AxgAEisjzI+zMBZAJAw4YNz5s+fbqzH4IO27t3\nL2rWrBnvZFQIzGvvJEpeN54xA6eOGwcA+KlnT/zUu3fE+zj15ZfRePZs/G/oUPzaRVuh1P3yS9T6\n5htsvvXWqNN2YlYWTp4yBasnTMCe00+Pah/B8rnul1+i+eOPY/1jj2FXq1ZHvAaA46dNw57TTz/8\n2npfrJ/LX5UdO9CuRw9s6NcP+T16hN2+/iefoMXIkYfH+NqYmYl8m2n569ix+MucOVj5zjs42KjR\nEetNYSHa9uqFopo1sfr114MGK/VWrsTZQ4fi+/79seXGGw8vt31Oi+D8227DvpNPxjq7VbMROmvI\nENT/4gtsb98e663x0mw4MSsLJ2Vl4fNp01DQsKEraYuVE9eOjIyM1WKnuZed4rdofxCmGhRASwAb\nAZwWYpt/Ahhs53isBnVXUlRjpAjmdZQibCAskiB5PWWKHB4CIS1NpH796KpwHnhApEoV54dQ2L1b\nq5U6d456FyHzecEC7WV5/vnaszAzU2TCBJF33hGZPVvkuee0AfvkyZoWN6q5rGrFNWvsv+eaa/Q9\n3bpFdqz8fO380adP4PWvvab7nTcv9H5KSkQ6dtTzxe87t31Or12rx5k40d72kbK+p4YNtbdouM9j\nKS4WOflk7YmcwFKiN6iECdYAnABgA4B25ZbXAFDL7+9PAXSxczwGa+5KiJtaBcG8jlIUbVfintez\nZumNLD29tKfhAw9EHowUF2s7oWuucSedzz+vaYsyvwLm86ZNOvTDMceUBqvhfqpVi7n9XEA9e2o6\nIu0ROWRIdIHjgAHa83TjxrLLDxzQ7/HCC+2NEbZ6tebLsGGHF9k+p59+Wt+7dav9dNvl/7+3YoUe\n56ij7OXTsmW6/TvvOJ8uB6VEmzVjzDQAnwFoZozJN8b0Mcb0Ncb09W0yEkB9AOONMWuMMV/4ljcE\n8Ikx5r8AVgKYJyIRTshGRBWS1ValRw9t05NA08UEtHSp9tZs0kSnwRkwQNshLV8eeZubFSuALVui\n7/0ZTr9+QOPG2qNRbDafCdQebelSndvxuut0xoRnn9V5OOvU0TkaGzQA5s4Ftm4Fvv0WWL1ap3/K\nydH3HDyovV1POcW5zyYCLFmi7bbs9Ii0plfKztbPGE3bwIcfDjxB/MSJ+j0++WT4tlqAzml5++3A\niy8C+fn2jw8Ac+YArVvrrBZO829H1q6dtj07dEinhwpnyhRtJ3nDDc6nK1nZieiS5Ycla+6KewlE\nBcK8jsG+fSJNm+qT+XnnhS2diFtef/aZSI0aImedVXa0/Fde0bSvXh3Z/vr311InJ8a3Cub11zVt\nOTn2tvcrXfk4J0d7QlqD7R5zjJYGTZ1qrzTUWv73v+vApyecoAPEOmHdOk3Tm2/a2z6K6vaABg7U\n/LBmRNi3T6sMO3SIbD8//qjV377xyGyd07/+qvn4+OORHStav/6qMw107Bj6f3LPHv2/+NvfvElX\nDFKmGtTrHwZr7mIA4R3mdZT27BE5+2y9tFkB2113hXxLXPL6q6+0Ku+UU0S2bSu7budODbr69bO/\nv6Iivcl37+5sOss7dEgHym3Z0n514eLFItWrS7EVpJ1xhlZvWcNj2Al8ygdwL7yg+zrzTJH9+2P/\nXC+9pPvbtCn2fUVi61b9rnv10tfWYLcffxz5vi65RIOvtWtLz+lQAeSkSXqs//wnmpRH59VX9ZjZ\n2cG3mTxZt/nkE8+SFS0GawzWEhIDCO/ElNdOPfU7tR+v7Nol0ry5XtaGD9en986d9fWDDwZ9m+fn\n9YYNOs9j48bBx9rq2VPH7Ao1xY6/pUvD3wSdMnWqHmvatPDb/vGHTotktTeLtrQk0Llozb953XUa\nrMaia1eRU0+NbR/RuvhiDbJWr9aOAldcEd3/2axZup/zz9dzOlx7zeuv1+mhIp07MxZFRSKtWum5\nv2dP4G0uvVS/Cy/TFSUGawzWEhKDNe/ElNd2GtlHU5rh5kCTsQaGv/2mVZ6VKon885+lyw8d0h6G\nxugciwG4fl77f7b8fJGTTtJAbNCg4O/56CO9PE+ebO8YmZladbRvX8zJDau4WKtu//rXwFMqWdav\n15tupUoiNWvKjz17On/+jB2r+fT3v0d/cz90SKc0iqQk00kzZuhnqFVLf48fH30+ZWaKAPJHixah\nO2EcOKDnS6TzlDrh00/1cz700JHrNm7UdU895X26osBgjcFaQqqQwVqcSpdizuvcXH1Kv/12bScy\nbpzIl1/qDXTjRpH33tP1c+dqoDNpkm736KMiL74o8vDDInffrRNap6VpiVW0NxC3A8Nff9XgoWrV\nwJM3792rAVvVqhoEleP6eW19llmzNB+POkqHoQj12UpKRJo100nFwzl0SL/LW25xLs3hzJmjt483\n3gi8fvZsDT7q1tXzKjfXXmlPNIYO1bSMGhXd+z/+WN8/c6ZzaYrUTTdpGk47Lbb82b9fq9atkswz\nz9SHl6+/1vXW/+KHH8rhoUHiUVreu7deV9avL7t85Eh9sPr5Z2/TEyUGawzWElKFDNaSdRqTkhKd\nmNnucAjlf9LTtYrkvPNK2361aBFd6UWwPJw7Vxt2z5+vY2rddpsGVM2aaUnHBx+E3/eWLSKnn64B\n0OLFwbf77Tfdrk4dbS/mx5PzeuFCvTlVrnw4eAlrzBjNd+tGG8yCBbrdrFnOpNWOkhIdWqJJEy2l\nsRQXa3Bgde4YNuzwZ7XVjiratNx5Z+jgMZTHHtPSP/9OHl7bvl0DtVgmIRc5/L/1c/fuWnLWsqUG\nP1ZbwZ49tcTt2mt1PLsFCzy5nh1h+3YN5C+/vPSaUlwscuKJIp06eZuWGDBYY7CWkCpksCYismSJ\nNgLu0cOzC1vMef3ss/rv3b69BgdPP63VgNOm6QCsr7+uVUgdO+p2N9ygn2v9eu1hZ11ArcAqI0O3\nCzaIZzi5uXpxbtZMA5aaNY8MENPSNK3+AWO3biLvvqs9HMuX0G3aJPKXv2gvuAAlZkfYtEnbyjRq\nVKatmCfntTWeVSQ3419/1TwI0d5ORLQEtHbtskGTF3xVbvLCC/p6924tCQQ0KCjX8N/VfD50SIOd\nSpXK9lS1ExhedJGWIMeT9X82YoQj87mWKcX817+0YX+HDpo/1nnYrFl8AjXL+PGajvfeK00/oG0i\nkwSDNQZrCanCBmtWlQ+g1YoeiLnNWnq6XogPHgw/FEKwG4T/+0pK9CkYKNsmzC6rZAnQtk79+ok8\n84xemFes0MmvFy/W4z36qAZ2N92kJTfWQKjt22tQ8uGH2ki/YUMtNXj1VfvpWLtW921VS4qLJT4W\na1iFKlUivxnfeKNWcQabWLygQD9Pz56OJdc26zyrXVsbx594on5X/fsHLIF1/foxb56eY1Wq6LAo\ndkrB//xTHx6GD3c3baE4VXrv9zAT9Jz+5RcNkpwoxYtVUZHIuefqA9fu3aWdapzo3esRBmsM1hJS\n0BMz2XoNRqplS30iPfZY/ZeZMMH1Q8Z0Ebj3Xk3niy+WLoumjVj57/XAAa1KTE+PrLv/Bx+UVgH+\n4x/hA0P/10uWaJui/v1L898aCd0YnZYnUsuX62dISxOZN8+9tlSWdu00zdOn6+tIjrVwYdn3lpeT\no+sDtdXzglU6Auj38dxzQTf15GFv5kz9X61WzV51s/UgFq/SJRFXrp8h89qJUjyn9O+v+d+3b+k0\nY0l072CwxmAtIQU9MePUrssT1s2oXz9tH2WV5rz9tquHjeki0LWrlsaEGvYh2hvEtm1a2tWkyZHj\ngwUyfbrePNPStNG5dZxoeqcWFmrw1qqVHO4BGK3Zs/V7TE+Xn267zb3z1QqmMjPLLrd7Q7La8QSb\nI/GOO7QNUkFBzEmN2jnn6GccMCDkZp6VzA8YoOmpVCl8ldr992vgH6zkMkklzbU6N1cDayvgf/XV\npLp3MFhjsJaQwj6t1amjpQhJ9M8WVsuW2lDXGhX+22+1qL5ePXfm0/OJ+iKwZo3+Wz/xhKPpKeM/\n/9Gn4PPPD91OasoUvWGedNKRI95H+/TsZKnAG2+U3iR8I787av9+nYy6efPYgqlRozSNGzaUXX7g\ngPa4dCPtdkXwfXgSrFnpGTSotNr9ySeDd4xp3lzHNUsxSVUL8v77+uBUv37S3TsYrDFYS0hBT8xv\nv9XeRdaNr2NHT9Plmh9/1Kq7IUPKLl+5srSn1a5drhw66ovALbfoDdztnm0zZ8rhNnyBboTjxun6\nTp3sD+wajtOlAr4HjMLq1TWtt97qbAnLiBG631gv6Js3a9Bbvl3V++/r/hcujG3/0Yrw+/BsiBTr\n+AsWaO9iQDthlA+Y8/N13Zgx7qYrDpKuffE990jc29BFgcEag7WEdMSJuWOHVjmkpWlVQvXqWpJg\njP25AxPZAw/oZ9u8+ch1ixZpu6dLL3WuF56dBsKhfPed3tSHDnUmPeE8+aReQp5+uuxya8iJbt2c\n7aHoZKmA3419eU6OyJVXappPPFGD8Vh99502dHeqQ0rXrtqLtbCwdNnNN+tn8F/mpQi/D08HH7Ys\nXVraMeayy3RGBUtWli7/8kt30xUHSRWsJVIbuggxWGOwljgCBRALFugUMnXqaHDQrZsWYefminzx\nhZ5W1au790/nRVH+H3/o8BJ33BF8G2vaneuvj326G5HSi9bSpdE1ev/b37QkwU5bMieUlJS2V5o1\nS18//ri+btky9Oj28RbovP6//9Mq7sqVdXywp56K7jyzprmqXdu572L2bM1Xq93f3r36PxZLuz2P\nxTWAmDJFH67OOKN02JaePXUy+WKbc5wmkaQJ1hKtDV2EGKwxWEsc/mP35Obq0481Vs9VV+mAneWD\np+7dtdHoyJGupyngaydYEyqHe+q2JoDu2rVsdWC0wePixSLp6bKvSZPIPtPmzXozuvfeyI8Ziw8/\n1NLHqlV1wnRA/w41QG2CKXPB/eMPrTIDtK2d/0wDds+z7Gx9/9ixziWysFBL1q6+Wl+/957EvRdj\nhOIeQFiN2evWFfn8c83PW26Jf5stF8Q9r+1KxDZ0EWCwxmAtseTmitSrJwcaNNBT5pRTQt+Mv/5a\nA7pQcx/G6q23tOp14EDnA7WCAh08NVgPvPJuu00ODwQqElvw+Nprcrjt3xln2C+xe+ABLRGySg28\n9K9/lQbw1appj80kEvCCO2+ejv9UqZKWYD38sL3vdPduPXdatXK+enL4cE3P5s06iPFxxzlTouuR\nhAggJk/WPKxcWc/XwYOTqiTHroTI6wqAwRqDtcRRUqLDV1gXt27d7N0gevXSEpZA7b1isXGjVk1a\nU6gAWmXlpHfe0f3On29v+5KS0jZPXbtGf/H//XetOktPl9/PO08Ol16Gm+Jpxw4NKO68M/JjOsUa\n2+3RR+OXhigFveDu3KnnsXWedeoUfqL0QYN0288+czqZOvE1oB1eqlUTue++ClcK4YiZM7U02BiR\no49OuUBNJIHyOsUxWGOwlhh27tQneN/UPz/fdJP9QOSHH7Razqk2NVu36lhnaWl6o7r5Zu31aJXm\nOHXBLSkROfts7dIfyTyYhYXaucIawT0aVo/aN97Qi4BVYhdugu4RI/TGU35SZK8kcQNhEZtD0px0\nkn4X9erpwK+BeriuXasPNffc405Cc3P1f8p6UHn55aTK74QKIIYNk2TsfWhXQuV1CmOwxmAt/las\nEDnhBK0yqFFDZMmSyBu99++vwVX58aFCKd+G4fffNVhJS9Offv20TZCVhuHD9TSuWdOZm9aSJbq/\nt96K7H25ufqUhrqIvQAAFQ5JREFUXrWqpjPSNltr1uhN+LrrRMR3ESgp0ZI6QNvQBfLnn9oG54Yb\nIjueU5K8gbBIBAOIvvSSBkuAzqYwZoyOZ5ebq9/VJZdoR5tZs9wr7bKGA6lVK3Xy2WtJ/nBhR8Lk\ndYpjsMZgLX6Ki3UohsqVtaTIqmqRKIaT2LpV25WF6lFZnnUhnTdPh4aoUUMOj91mBX3+AV1Rka5L\nTw87grotV16psxREMt6W/03d6iEaSW9Y/xv977+LiF9eFxVpKSIg8uabR77XmrB91Sr76XVSkjcQ\nFoliANG+ffWcA7TUrUYNbTvpRRuogwe1qjwJS4USIoBIgYcLOxIirysABmsM1rxR/ma0bZtOrAto\ngFBuwNeoTsyhQ7XEaO1a++8ZM6a0qqdKlfClXDt2iBx/vI6R9dtvkafRsm6dHB7xPBL++VhSItKj\nh5ZI3n+/vfdPm6bHff31w4vK5HVBgY6yXqmStrex7N+vgWWnTpGll8qI+oL7ySea91abtsaN3b/x\nW8HFo48mXZCREAFECjxc2JEQeV0BMFhjsOYN/6fKBQu0Og3QRtIB2mtFdWJajeZ91XthjRunpXrH\nHCMRlR78+98a2F1xRfQ95Hr31pLAWAI+EX1/o0YiZ54ZflDYPXv0Jn/uuWXSfURe790rcuGF+hmt\nKlZrlgBemGMS8wX3009Lx5tzs7QryUuFGEB4h3ntDS+DtUqgiisjA5g4EbjqKqBLF2DPHmDSJOC5\n5wBjnDnG0UcDQ4YAs2cDK1cG366oCLjvPqB/f6BNG6CkBBgxApgwAcjLC3+ctm2BV14BFi4ERo2K\nPJ2//AK8+y5w991A/fqRv99f/frAW28BX3+tnyGUp58GtmzRtFeuHHy7GjWAefM0P7t1Az79FBg9\nGrjwQs2r0aNjSzNF7+BBID8/svM1GqtWAdnZ+n8L6O/sbF1ORKnNTkSXLD8sWYtAQYH2aqtdu7TK\n8eGHQ74l6qeI3bu1pCzYnKE7d5a2AbKm0Imm9KCkpHRg1rlzI0vjI49oPnz/fWTvC6VvX93nRx8F\nXv/991pSZo3P5idoXltjmlnjmj31VFKVriSimJ6Ok7y0y0ss7fEO89o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      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE \n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = error_rate.plot(x='N', y='rmse', style='bx-', grid=True)\n",
    "ax = error_rate.plot(x='N', y='mape_pct', style='rx-', grid=True, ax=ax)\n",
    "ax.set_xlabel(\"N\")\n",
    "ax.set_ylabel(\"RMSE/MAPE(%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.578\n",
      "min MAPE = 0.793%\n",
      "optimum N = 3\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "N_opt = temp['N'].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(N_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - epochs and batch_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7d38848efc2c4a878ec3bcaedb43e130",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "229723117c994dafa99e317d77960f05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "099547b77ce34e9fa70632b26c7565b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d97858f23544354b5f9ef5ac7ea6e6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fa58006a01524d8f81d85a168c383bd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b391d5d404644ae986e087bbfbc22cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "763b6f1d2fcb4a1785c182ed9219f54e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 27.215002902348836\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epochs</th>\n",
       "      <th>batch_size</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1.667514</td>\n",
       "      <td>0.838295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1.480032</td>\n",
       "      <td>0.739401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>1.425106</td>\n",
       "      <td>0.711618</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>64</td>\n",
       "      <td>1.416096</td>\n",
       "      <td>0.706658</td>\n",
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       "    <tr>\n",
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       "      <td>1.409261</td>\n",
       "      <td>0.702708</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1.552082</td>\n",
       "      <td>0.780375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>16</td>\n",
       "      <td>1.573186</td>\n",
       "      <td>0.795431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>32</td>\n",
       "      <td>1.588718</td>\n",
       "      <td>0.802869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>64</td>\n",
       "      <td>1.598122</td>\n",
       "      <td>0.807779</td>\n",
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       "      <td>1.598143</td>\n",
       "      <td>0.808665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>1.498005</td>\n",
       "      <td>0.750511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>1.558468</td>\n",
       "      <td>0.785329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>1.575044</td>\n",
       "      <td>0.796978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "      <td>1.586323</td>\n",
       "      <td>0.801415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>20</td>\n",
       "      <td>128</td>\n",
       "      <td>1.591745</td>\n",
       "      <td>0.802913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>1.455514</td>\n",
       "      <td>0.724324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>1.536108</td>\n",
       "      <td>0.772672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30</td>\n",
       "      <td>32</td>\n",
       "      <td>1.559357</td>\n",
       "      <td>0.784155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>30</td>\n",
       "      <td>64</td>\n",
       "      <td>1.579724</td>\n",
       "      <td>0.796796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>128</td>\n",
       "      <td>1.588717</td>\n",
       "      <td>0.802573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>8</td>\n",
       "      <td>1.398495</td>\n",
       "      <td>0.694885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40</td>\n",
       "      <td>16</td>\n",
       "      <td>1.504592</td>\n",
       "      <td>0.753171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>1.547742</td>\n",
       "      <td>0.777191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>40</td>\n",
       "      <td>64</td>\n",
       "      <td>1.566035</td>\n",
       "      <td>0.792756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40</td>\n",
       "      <td>128</td>\n",
       "      <td>1.582148</td>\n",
       "      <td>0.797650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>1.336994</td>\n",
       "      <td>0.656068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>50</td>\n",
       "      <td>16</td>\n",
       "      <td>1.480901</td>\n",
       "      <td>0.738853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>1.518795</td>\n",
       "      <td>0.760805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50</td>\n",
       "      <td>64</td>\n",
       "      <td>1.576257</td>\n",
       "      <td>0.794397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "      <td>1.577667</td>\n",
       "      <td>0.795226</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    epochs  batch_size      rmse  mape_pct\n",
       "0        1           8  1.667514  0.838295\n",
       "1        1          16  1.480032  0.739401\n",
       "2        1          32  1.425106  0.711618\n",
       "3        1          64  1.416096  0.706658\n",
       "4        1         128  1.409261  0.702708\n",
       "5       10           8  1.552082  0.780375\n",
       "6       10          16  1.573186  0.795431\n",
       "7       10          32  1.588718  0.802869\n",
       "8       10          64  1.598122  0.807779\n",
       "9       10         128  1.598143  0.808665\n",
       "10      20           8  1.498005  0.750511\n",
       "11      20          16  1.558468  0.785329\n",
       "12      20          32  1.575044  0.796978\n",
       "13      20          64  1.586323  0.801415\n",
       "14      20         128  1.591745  0.802913\n",
       "15      30           8  1.455514  0.724324\n",
       "16      30          16  1.536108  0.772672\n",
       "17      30          32  1.559357  0.784155\n",
       "18      30          64  1.579724  0.796796\n",
       "19      30         128  1.588717  0.802573\n",
       "20      40           8  1.398495  0.694885\n",
       "21      40          16  1.504592  0.753171\n",
       "22      40          32  1.547742  0.777191\n",
       "23      40          64  1.566035  0.792756\n",
       "24      40         128  1.582148  0.797650\n",
       "25      50           8  1.336994  0.656068\n",
       "26      50          16  1.480901  0.738853\n",
       "27      50          32  1.518795  0.760805\n",
       "28      50          64  1.576257  0.794397\n",
       "29      50         128  1.577667  0.795226"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'epochs'\n",
    "param_list = [1, 10, 20, 30, 40, 50]\n",
    "\n",
    "param2_label = 'batch_size'\n",
    "param2_list = [8, 16, 32, 64, 128]\n",
    "\n",
    "# Split train into x and y\n",
    "x_train_scaled, y_train_scaled = get_x_y(train_scaled, N_opt, N_opt)\n",
    "\n",
    "# Split cv into x and y\n",
    "x_cv_scaled, y_cv, mu_cv_list, std_cv_list = get_x_scaled_y(np.array(train_cv['adj_close']).reshape(-1,1), N_opt, num_train)\n",
    "\n",
    "error_rate = {param_label: [], param2_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    for param2 in tqdm_notebook(param2_list):\n",
    "    \n",
    "        # Train, predict and eval model\n",
    "        rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                              y_train_scaled, \\\n",
    "                                              x_cv_scaled, \\\n",
    "                                              y_cv, \\\n",
    "                                              mu_cv_list, \\\n",
    "                                              std_cv_list, \\\n",
    "                                              lstm_units=lstm_units, \\\n",
    "                                              dropout_prob=dropout_prob, \\\n",
    "                                              optimizer=optimizer, \\\n",
    "                                              epochs=param, \\\n",
    "                                              batch_size=param2)\n",
    "    \n",
    "        # Collect results\n",
    "        error_rate[param_label].append(param)\n",
    "        error_rate[param2_label].append(param2)\n",
    "        error_rate['rmse'].append(rmse)\n",
    "        error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ad15dce10>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O61J4KOyvZDgLHuXf+/FqBhERETVs7mxJogYoKgp46y15LMO8ecCkSW4qxCsfyPey3x5w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\nEYONialoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot performance versus params\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "temp = error_rate[error_rate[param2_label]==param2_list[0]]\n",
    "ax = temp.plot(x=param_label, y='rmse', style='bs-', grid=True)\n",
    "legend_list = [param2_label + '_' + str(param2_list[0])]\n",
    "\n",
    "color_list = ['r', 'g', 'k', 'y', 'm', 'c', '0.75']\n",
    "for i in range(1,len(param2_list)):\n",
    "    temp = error_rate[error_rate[param2_label]==param2_list[i]]\n",
    "    ax = temp.plot(x=param_label, y='rmse', color=color_list[i%len(color_list)], marker='s', grid=True, ax=ax)\n",
    "    legend_list.append(param2_label + '_' + str(param2_list[i]))\n",
    "\n",
    "ax.set_xlabel(param_label)\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "plt.legend(legend_list, loc='center left', bbox_to_anchor=(1.0, 0.5)) # positions legend outside figure\n",
    "# ax.set_xlim([10, 50])\n",
    "# ax.set_ylim([0, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.337\n",
      "min MAPE = 0.656%\n",
      "optimum epochs = 50\n",
      "optimum batch_size = 8\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "epochs_opt = temp[param_label].values[0]\n",
    "batch_size_opt = temp[param2_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(epochs_opt))\n",
    "print(\"optimum \" + param2_label + \" = \" + str(batch_size_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - LSTM units and dropout prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
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    {
     "name": "stdout",
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     "text": [
      "Minutes taken = 46.122497681776686\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lstm_units</th>\n",
       "      <th>dropout_prob</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.553387</td>\n",
       "      <td>0.780392</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.558436</td>\n",
       "      <td>0.782333</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.576501</td>\n",
       "      <td>0.787542</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.544457</td>\n",
       "      <td>0.774693</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.483881</td>\n",
       "      <td>0.741902</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.468185</td>\n",
       "      <td>0.730520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.480588</td>\n",
       "      <td>0.738882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.508299</td>\n",
       "      <td>0.752703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.525493</td>\n",
       "      <td>0.767056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>50</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.560423</td>\n",
       "      <td>0.783796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>50</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.582963</td>\n",
       "      <td>0.803620</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>50</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.356429</td>\n",
       "      <td>0.667560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>64</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.474909</td>\n",
       "      <td>0.733837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>64</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.500676</td>\n",
       "      <td>0.747508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>64</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.542878</td>\n",
       "      <td>0.774681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>64</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.558148</td>\n",
       "      <td>0.782893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>64</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.589355</td>\n",
       "      <td>0.808696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>64</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.284141</td>\n",
       "      <td>0.623964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>128</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.437774</td>\n",
       "      <td>0.711271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>128</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.471207</td>\n",
       "      <td>0.733909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>128</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.503506</td>\n",
       "      <td>0.752546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>128</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.564039</td>\n",
       "      <td>0.790391</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>128</td>\n",
       "      <td>0.9</td>\n",
       "      <td>1.583945</td>\n",
       "      <td>0.799156</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>128</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.208167</td>\n",
       "      <td>0.583837</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    lstm_units  dropout_prob      rmse  mape_pct\n",
       "0           10           0.5  1.553387  0.780392\n",
       "1           10           0.6  1.558436  0.782333\n",
       "2           10           0.7  1.576501  0.787542\n",
       "3           10           0.8  1.544457  0.774693\n",
       "4           10           0.9  1.483881  0.741902\n",
       "5           10           1.0  1.468185  0.730520\n",
       "6           50           0.5  1.480588  0.738882\n",
       "7           50           0.6  1.508299  0.752703\n",
       "8           50           0.7  1.525493  0.767056\n",
       "9           50           0.8  1.560423  0.783796\n",
       "10          50           0.9  1.582963  0.803620\n",
       "11          50           1.0  1.356429  0.667560\n",
       "12          64           0.5  1.474909  0.733837\n",
       "13          64           0.6  1.500676  0.747508\n",
       "14          64           0.7  1.542878  0.774681\n",
       "15          64           0.8  1.558148  0.782893\n",
       "16          64           0.9  1.589355  0.808696\n",
       "17          64           1.0  1.284141  0.623964\n",
       "18         128           0.5  1.437774  0.711271\n",
       "19         128           0.6  1.471207  0.733909\n",
       "20         128           0.7  1.503506  0.752546\n",
       "21         128           0.8  1.564039  0.790391\n",
       "22         128           0.9  1.583945  0.799156\n",
       "23         128           1.0  1.208167  0.583837"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'lstm_units'\n",
    "param_list = [10, 50, 64, 128]\n",
    "\n",
    "param2_label = 'dropout_prob'\n",
    "param2_list = [0.5, 0.6, 0.7, 0.8, 0.9, 1]\n",
    "\n",
    "error_rate = {param_label: [], param2_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "    \n",
    "    for param2 in tqdm_notebook(param2_list):\n",
    "    \n",
    "        # Train, predict and eval model\n",
    "        rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                              y_train_scaled, \\\n",
    "                                              x_cv_scaled, \\\n",
    "                                              y_cv, \\\n",
    "                                              mu_cv_list, \\\n",
    "                                              std_cv_list, \\\n",
    "                                              lstm_units=param, \\\n",
    "                                              dropout_prob=param2, \\\n",
    "                                              optimizer=optimizer, \\\n",
    "                                              epochs=epochs_opt, \\\n",
    "                                              batch_size=batch_size_opt)\n",
    "    \n",
    "        # Collect results\n",
    "        error_rate[param_label].append(param)\n",
    "        error_rate[param2_label].append(param2)\n",
    "        error_rate['rmse'].append(rmse)\n",
    "        error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1af43cb160>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FvxgMvT66z/cSIcTwSEixE1sBpb/7XV33OpY1a9ZY1bEsX76c5cuXSx1LH0wnTPAc7Hx9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\nAAAgoVCkAAAAAEgo/w/FHVYHm10UAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot performance versus params\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "temp = error_rate[error_rate[param2_label]==param2_list[0]]\n",
    "ax = temp.plot(x=param_label, y='rmse', style='bs-', grid=True)\n",
    "legend_list = [param2_label + '_' + str(param2_list[0])]\n",
    "\n",
    "color_list = ['r', 'g', 'k', 'y', 'm', 'c', '0.75']\n",
    "for i in range(1,len(param2_list)):\n",
    "    temp = error_rate[error_rate[param2_label]==param2_list[i]]\n",
    "    ax = temp.plot(x=param_label, y='rmse', color=color_list[i%len(color_list)], marker='s', grid=True, ax=ax)\n",
    "    legend_list.append(param2_label + '_' + str(param2_list[i]))\n",
    "\n",
    "ax.set_xlabel(param_label)\n",
    "ax.set_ylabel(\"RMSE\")\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "plt.legend(legend_list, loc='center left', bbox_to_anchor=(1.0, 0.5)) # positions legend outside figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.208\n",
      "min MAPE = 0.584%\n",
      "optimum lstm_units = 128\n",
      "optimum dropout_prob = 1.0\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "lstm_units_opt = temp[param_label].values[0]\n",
    "dropout_prob_opt = temp[param2_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(lstm_units_opt))\n",
    "print(\"optimum \" + param2_label + \" = \" + str(dropout_prob_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuning model - optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f9b3c2429ce4fce8cd8144d08b36085",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minutes taken = 17.31384893655777\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optimizer</th>\n",
       "      <th>rmse</th>\n",
       "      <th>mape_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>adam</td>\n",
       "      <td>1.208825</td>\n",
       "      <td>0.583952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sgd</td>\n",
       "      <td>1.485740</td>\n",
       "      <td>0.741810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmsprop</td>\n",
       "      <td>1.175873</td>\n",
       "      <td>0.580122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adagrad</td>\n",
       "      <td>1.389520</td>\n",
       "      <td>0.687648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>adadelta</td>\n",
       "      <td>1.360287</td>\n",
       "      <td>0.671603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>adamax</td>\n",
       "      <td>1.252718</td>\n",
       "      <td>0.601848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nadam</td>\n",
       "      <td>1.160528</td>\n",
       "      <td>0.578567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  optimizer      rmse  mape_pct\n",
       "0      adam  1.208825  0.583952\n",
       "1       sgd  1.485740  0.741810\n",
       "2   rmsprop  1.175873  0.580122\n",
       "3   adagrad  1.389520  0.687648\n",
       "4  adadelta  1.360287  0.671603\n",
       "5    adamax  1.252718  0.601848\n",
       "6     nadam  1.160528  0.578567"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_label = 'optimizer'\n",
    "param_list = ['adam', 'sgd', 'rmsprop', 'adagrad', 'adadelta', 'adamax', 'nadam']\n",
    "\n",
    "error_rate = {param_label: [], 'rmse': [], 'mape_pct': []}\n",
    "tic = time.time()\n",
    "for param in tqdm_notebook(param_list):\n",
    "   \n",
    "    # Train, predict and eval model\n",
    "    rmse, mape, _ = train_pred_eval_model(x_train_scaled, \\\n",
    "                                          y_train_scaled, \\\n",
    "                                          x_cv_scaled, \\\n",
    "                                          y_cv, \\\n",
    "                                          mu_cv_list, \\\n",
    "                                          std_cv_list, \\\n",
    "                                          lstm_units=lstm_units_opt, \\\n",
    "                                          dropout_prob=dropout_prob_opt, \\\n",
    "                                          optimizer=param, \\\n",
    "                                          epochs=epochs_opt, \\\n",
    "                                          batch_size=batch_size_opt)\n",
    "    \n",
    "    # Collect results\n",
    "    error_rate[param_label].append(param)\n",
    "    error_rate['rmse'].append(rmse)\n",
    "    error_rate['mape_pct'].append(mape)\n",
    "    \n",
    "error_rate = pd.DataFrame(error_rate)\n",
    "toc = time.time()\n",
    "print(\"Minutes taken = \" + str((toc-tic)/60.0))\n",
    "error_rate   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'RMSE/MAPE(%)')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W9dfbtPuHHwYdmciBKXmLU7/9BjfcYMUo+/ULOprE9p//wGWXWful1auDjiYx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\nQmCC\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot RMSE \n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "ax = error_rate.plot(x='optimizer', y='rmse', style='bx-', grid=True)\n",
    "ax = error_rate.plot(x='optimizer', y='mape_pct', style='rx-', grid=True, ax=ax)\n",
    "ax.set_xticklabels(param_list)\n",
    "ax.set_xlabel(\"Optimizer\")\n",
    "ax.set_ylabel(\"RMSE/MAPE(%)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min RMSE = 1.161\n",
      "min MAPE = 0.579%\n",
      "optimum optimizer = nadam\n"
     ]
    }
   ],
   "source": [
    "# Get optimum value for param and param2\n",
    "temp = error_rate[error_rate['rmse'] == error_rate['rmse'].min()]\n",
    "optimizer_opt = temp[param_label].values[0]\n",
    "print(\"min RMSE = %0.3f\" % error_rate['rmse'].min())\n",
    "print(\"min MAPE = %0.3f%%\" % error_rate['mape_pct'].min())\n",
    "print(\"optimum \" + param_label + \" = \" + str(optimizer_opt))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tuned params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param</th>\n",
       "      <th>original</th>\n",
       "      <th>after_tuning</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lstm_units</td>\n",
       "      <td>50</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dropout_prob</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>optimizer</td>\n",
       "      <td>adam</td>\n",
       "      <td>nadam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>epochs</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>batch_size</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rmse</td>\n",
       "      <td>2.22106</td>\n",
       "      <td>1.16053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mape_pct</td>\n",
       "      <td>1.13407</td>\n",
       "      <td>0.578567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          param original after_tuning\n",
       "0             N        9            3\n",
       "1    lstm_units       50          128\n",
       "2  dropout_prob        1            1\n",
       "3     optimizer     adam        nadam\n",
       "4        epochs        1           50\n",
       "5    batch_size        1            8\n",
       "6          rmse  2.22106      1.16053\n",
       "7      mape_pct  1.13407     0.578567"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = {'param': ['N', 'lstm_units', 'dropout_prob', 'optimizer', 'epochs', 'batch_size', 'rmse', 'mape_pct'],\n",
    "     'original': [N, lstm_units, dropout_prob, optimizer, epochs, batch_size, rmse_bef_tuning, mape_pct_bef_tuning],\n",
    "     'after_tuning': [N_opt, lstm_units_opt, dropout_prob_opt, optimizer_opt, epochs_opt, batch_size_opt, error_rate['rmse'].min(), error_rate['mape_pct'].min()]}\n",
    "tuned_params = pd.DataFrame(d)\n",
    "tuned_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on test set = 1.164\n",
      "MAPE on test set = 0.583%\n"
     ]
    }
   ],
   "source": [
    "# Split train_cv into x and y\n",
    "x_train_cv_scaled, y_train_cv_scaled = get_x_y(train_cv_scaled_final, N_opt, N_opt)\n",
    "\n",
    "# Split test into x and y\n",
    "x_test_scaled, y_test, mu_test_list, std_test_list = get_x_scaled_y(np.array(df['adj_close']).reshape(-1,1), N_opt, num_train+num_cv)\n",
    "\n",
    "# Train, predict and eval model\n",
    "rmse, mape, est = train_pred_eval_model(x_train_cv_scaled, \\\n",
    "                                        y_train_cv_scaled, \\\n",
    "                                        x_test_scaled, \\\n",
    "                                        y_test, \\\n",
    "                                        mu_test_list, \\\n",
    "                                        std_test_list, \\\n",
    "                                        lstm_units=lstm_units_opt, \\\n",
    "                                        dropout_prob=dropout_prob_opt, \\\n",
    "                                        optimizer=optimizer_opt, \\\n",
    "                                        epochs=epochs_opt, \\\n",
    "                                        batch_size=batch_size_opt)\n",
    "\n",
    "# Calculate RMSE\n",
    "print(\"RMSE on test set = %0.3f\" % rmse)\n",
    "\n",
    "# Calculate MAPE\n",
    "print(\"MAPE on test set = %0.3f%%\" % mape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'USD')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n7crBF2OO6g10K3P73Ey+CDbc/SvjgsNBx8SO5CWAveB4ldMd9E4jWxffd5DM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\nlMyJiIiIJJiSOREREZEE+/9CT9gOZqr2bQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "\n",
    "est_df = pd.DataFrame({'est': est.reshape(-1), \n",
    "                       'date': df[num_train+num_cv:]['date']})\n",
    "\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'predictions'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Zoom in to test set')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NOXnyJJcvXwbIEeBs2bKFo0ePUqZMGXx8fHBzc8Pd3Z2LFy9y+PBhAPbt20fz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\n2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9BRTV5DwwaNrBUREZEeQ0leCy2XNEvoaAqVXFy3VkRERLKXkrwWUtXkxePluNfR3Fzf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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time, for test set only\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "ax = train.plot(x='date', y='adj_close', style='b-', grid=True)\n",
    "ax = cv.plot(x='date', y='adj_close', style='y-', grid=True, ax=ax)\n",
    "ax = test.plot(x='date', y='adj_close', style='g-', grid=True, ax=ax)\n",
    "ax = est_df.plot(x='date', y='est', style='r-', grid=True, ax=ax)\n",
    "ax.legend(['train', 'dev', 'test', 'predictions'])\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")\n",
    "ax.set_xlim([date(2018, 4, 1), date(2018, 11, 30)])\n",
    "ax.set_ylim([130, 155])\n",
    "ax.set_title(\"Zoom in to test set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(130, 155)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cNtB76HmrwlvE3o+l363ylFqzFT77DGxtYc4cbYut7dvxdffl3Rrv\nEnQkiHPV3YlWYgEw/2CASQQ5gCpOVQA4FH3owUFFgdmztd/feQeSk3OhZiKvkjAnhBAvsdSdBSJv\nRTL3wFz6VO/D0StHmbRtkjbpwGDQgsG+fZnuppAdDOEGNoRvoH1UIT6auo09334C48bBypVQoADc\nvYvaqBF7qxYh9PeJuBRwoe+iM5S6qUK9ejBjhsnM5kwNc4ejD6c/4eYGU6Zok0dmzsyFmom8SsKc\nEEK8xFK7LYesG0KymkwD1wb4BfvhU9JHm3Tg5wcLFmjdnba2L2TbqLQzNQPsW+HXCfT/jTHOLE1a\nEszW1tX4w1uh1pEY1i1QGB18lSE74WxhMxJOHNWWFMmDy4m8CE52TjjZOWUMcwDvvQdeXjBkyIN9\nW0H2bjVxEuaEEOIlpvfQ82vbXwk6GkTFYhUZtG7Qg1meqTNBP/hAKzx79gsZl5Z2Zuma9lUJr+5G\nEZsi7I7czd6Le6lwpD+NXP9hiX9rtk/9mFhzlXe3x6MoCq5Jdvh1VDG0r5HnlhN5kao4VUnfzZpK\nUWDsWLh3D9q311pVZe9WkydhTgghXnJKyiLAh6MP0792//QzOl99Fayttd8HvJhxaalLlADUcanD\n9bjrRNyM4N///qX2z7U5E3OGgMYBLOu8jK11inMw5E+oVAlUFd1Hgxk0Yrm2pEgeW07kRapSrApH\noo+QrD5ibFznztpEiIMHoWVL2btVSJgTQoiX3fLjywHwr+/P9LDpWvdmqk8/hStXoGNHbRzWC+7G\n1HvoWdp5KeaKOatOrcLKwoq/uvzFaN/RKIqCfwN/GsQXg+ho49Zb+ohHLzXyMqvqXJW7iXc5d+Pc\nowtMmgRFi8L69dCixYMgJ92tJknCnBBCvMQM4Qb+OPgHxe2K89VrXxHUMQi/YD8t0K1Zow2or1hR\na9nJoW2umpVpRn+f/gAMrz+cNuXbpKmw4UFLUx7eeutFmrRtEvfu3wMezGg1hBu0SSupQkIgIUHr\ndv3jD215F+luNVkS5oQQ4iUWGhWKk50Tr7i+AmgtY8adEKZN08ZczZyphYIc2ubKEG5g0eFFjHp1\nVMaWwtDQjAsDm9BYOdAmrYwOGQ1oXeOpE0h8SqaEtNTQtny5FuIUBXr1gnbtpLvVREmYE0KIl9jA\nOgOJvB1JNadqxmN6Dz3+5d6G7du1DdwbNXpwwQsel5Z2ZusY/Zj0LYWgffbDYcSExspB+kWW5x6Y\na3xeqeMOQxZ/zb5pn2rPpXt37aWqXCtoKUHOREmYE0KIl9iRK9ogeu/i3ulPfPklxMbCxIk5Wp90\ne6byUEuhMNJ76ClXpBwnr50kJi6Gzw2fM2z9MIKPBhM9oDfNIydoAdhgIGH1Ci46KBSOuq6tQSdM\njoQ5IYR4iR24dACAas4PWuY4fRp++knbSaBChRytT9qZran0HnqTm+DwJIZwA1fuXqFDxQ5Ymlty\nM/4mP+z+gU6LO9E5uDMAE8Y253a7lvh1VLn56ccoScnQoYNJjS8UGglzQgjxEkjd6SEtQ7iB3/b/\nhp3OjjKOZR6cGDkSLC0hICBnKymyJLUrenGnxQT7BbOq2you373M313/Zve7u5nWYhqvlXmNOlEK\nbdvfw6vD+1QYGAj29lC3rkmNLxQai9yugBBCiGc3adskfEr6GHd6COoYBMCiw4tYenwpJR1KUtW5\nKmZKyr/dd+/WBsl/8QWUKJGLNReZeVxXtH8Df3xcfKjqVJU3Gi0j/n4iO0N/oN+mW5T19SVpwz/8\nMLgeg0BroQsNNanxhqZKwpwQQuRjqSFuc3QrJpVoyet/vM7HW+4T6W7DOt8x/DV/FJc/6Kn9xb57\nt7YciZMTDBuW21UXmXhUl7PeQ28Md6ktd+OajOPzTZ8Tdz+OwdfmszgkGev4e7TcdQ2s0izxIl56\n0s0qhBD5mN5Dz9x2cxl49Xdaj5pHg9OJhBdSWTT/Ht4fBLLFKY4W5621v9iTkmDzZhg9Ghwccrvq\n4hmlttwNqTeEpX5LMVPMCPFQaN85mWRzc8rNWiq7QpgYCXNCCJHPJSQlsMldxa8T/L0IgoLBPi4J\ns1u3+WMJtPb/hf1TP+HqrGnaJu1eXrJLQD6WdhJJC68WtK/QnjuJd3Bq3RmzypUhMhL695cgZ0Ik\nzAkhRD43M2wmALu8bLjsoO3DutEdDjiDyx0wj41lz/RRFI2I1tYk69ZNdgl4SRjCDWwM3wjAtVXB\nJJ04Dra2MH26zGo1IRLmhBAiH9twdgPrzqyjrGNZtpcKoOw1lX0u5tS9bIH7DVhRXoFklXd2xIOL\nC/zwg3S/vSRSx85NajYJ33D4czGsKKdyX1FNchs0UyZhTggh8rFlx5ahovKzfVeqDxqPAhTs3BML\nMwsUBaa+ovLPu020LZ+k++2lkjp27lW3V/GJhB2TB1O9cWfM4uLB19fktkEzZRLmhBAiH3OwcsDC\nzIJ6Fy2gVSuwsKBMQXeO/TyOXj0L4G+p5yezMBLtbaFpU+l+e4mkjp2z0dnwdUOIqOmBe8lKmCWr\nkJBgctugmTIJc0IIkc+kXSB4xckVNHZrzM5ur3IpbDPUq4ehRyOaR05g0IjltOw6iqBghU5dzTHM\nGindby8hW50tAHGJcdp4OdC2ahMmQ8KcEEJkUWa7LEzalrMzQ1PXlvvj4B8cvXKUCkUq0H9OR4qf\njITmzdMvOhsaimXwMgaNWK7tf6rXS/fbS8bGwgaAuPsS5kyVhDkhhMii1BCVGuhSB6D7lMzZmaGp\nOwL0XdmX4Vvh/F+/s8Sxv3by9dfxT/BBvzglrPn7g16ffv9T6X57qdjoUsJcYhzYaL8TF5eLNRI5\nTXaAEEKILEoNUe3+bMfHWxIJLQlBI1cZ1/zat2AqN7duwPen1S+0HkevHGXKjinEJsYS6gIrg5Kw\nqxsKRYrAzZvQtaus/G9CzBQzLM0tpWXOhEnLnBBCPAW9hx47nR2bneKYszCOG2uWAVqQK9d7KC6O\n7ukvMBiybYHe6LvR9F/Zn2rTq2GIMGCns6Nej0/5n58F6j//gLPzgyAnM1ZNio2FjYyZM2HSMieE\nEE9h3oF5XLxzkZL1a9GZPawd+D0HJ/+Ka3QsUcP74/XzYk4BkTER+HYa/kz7Y07aNgmfkj7GFr+4\nxDgGrhnIvIPzSEpOonW51mz9byuLOy1G76Fnf1RhlFnD4ehRGDVKgpwJstHZaC1zBSXMmSJpmRNC\niCwyhBvot6ofAMu7LOezzj9gmQzVwu9yX1G5+9qrXGheH8/x0/EOu/DM+2Omjs3beHYj8w/Ox32a\nO7P3zaZ2ydoc+eAI9V3rG4McQPVZf6MqCjv+5ytLj5goGwsb6WY1YdIyJ4QQWbQ7cjdFbIrgU9KH\nUgVKYTF9KSpwxEVH5chEiuu7Gss6hh6CAQOeqZUsdWxe24VtuZN4BwszC6Y0n8KQekMA8C+aZvLC\n4sXw778onTtTb94iLcjJJusmx0Znk34ChIQ5kyItc0II8QSpS5I0L9uc87fO07VKVxZ/149iKzdx\nq0ZFqlxIYEft4gCccLUFR0fQ6WDWrGduJdN76ClkUwiAEQ1GGIOcVqFJD+47a5b2s3Vr7bgsPWKS\nMrTMyWxWkyJhTgghniC12/OrbV9hYWaBs70zsXN/wVyFgp9/qU1+OHaZdWWg3PlYTvXvAmPHaqvw\nt2//TIFuydElXLh1gVdLv8qMPTPSr2/n46O1vm3YAMeOQa1a8PHH2nGQpUdMkLFlTrpZTZKEOSGE\neAK9h55FHRbh9nMQnaOdeG/Fe7Qs3hCKFuX07nWUe3so53+ezDHvEnzTsRSO02awv7gCZctCoUKw\na9dTfZ4h3EDvv3oDMK3lNII6BqVb387Y+vbWW3DhApw6Jd2qJs7GwobYxFgJcyZKxswJIUQWHIo+\nxO6SKkG/RrH+Uz+cNiyH1q0pPe03zg3rT41uQ/jW9gD/nPkHffvJ3Ny6AaZMgXbtwN7+qT5L+fpr\netq5ssolFm9nbxRFYb3Lp9wZPwHa3oG1a7XX7dvaBR9+KEHOxNnqbLl897KMmTNR0jInhBBPsPPC\nToauH8p2T0tWjPkfb44N0rpQN23CcvU6vMb9BEA1p2pcvHORUm/20BYObtsWmjWDL76Aa9ceffO0\n499S1HNvxKRpxxgaVxPl0CHo25cavUfQ6LdN2j3nztXWlLO317pXf/5ZZrCaOGM3q4UFWFpKmDMx\nEuaEEHlebu6Jei32Gm0XtgUguFMwfYbOQynlCkBEjzbpWsS8i3sDcPDyQe2AosA338CtWzB6tLFc\nuu+TMv5t34KpvDH/DfYtmAoTJ/K5HvqPWwve3lpYc3WFoUNh40ZYskTrWv37b5g6Veti9fOTQGfC\njBMgQGudkzBnUiTMCSHyvHR7ok6axL4FU9PviZqNuyyklawm02NZD67HXeenN36iTfk2sGkTduEX\nuFPeA6d5S9MFqGrO1QA4cPnAg5tUqQL9+2vrvx06lPH76PXsm/Ypnn2GMv3bU7i+P4xR/coxp0kh\n7pYtDcD5tzvAmTPw1VfQpAkcOJB+jJzMYDV5xh0gQBs3J7NZTYqEOSFEnpe67lqHoA70v/wrru8P\nY73Lp9qiuanrqvlk/2b3E7dOZM3pNXzf8nv61u6rfVanTqCq2A8aju3SFelaxJzsnChuX/xBy1yq\nwEBtIsTgwaCq6b6P/Xh7ml0Yh2WBwriFnebW292YbLWHty46Yn3kBAmFC+G6YnP6Vjd//4xj5GQG\nq0kz7gABWpiTljmTImFOCJEv6D30VHWqyowCJ/iiX3lqfDAG3n//hS2Qawg3MMowiq5VutKvtrbr\nA6Gh0C/l9/r1H9ki5u3snb5lDqBwYRgzBjZtgr/+Mn6fOi51uJt4l8E3KmJ15ToAbgtWM2i7ysRZ\n4dx2dsTylfrSjSqeKLVlTlVVCXMmSMKcECJfMIQb2BWpLfEx3eE48UkJ2oK5/fphcCdbx89dunOJ\nrku64lXYi5mtZ6IoinbC3x9u3tQmHlSpoh17qEWsmnM1jl45SmJSYvqb9u0LlStr497i4zGEG9h8\nbjO+4TDwm23GYqv7NWWsATZ0ro3ZjRtEOlpIN6p4IhudDSoqCUkJEuZMkIQ5IUSeZwg34Bfsh2dh\nTyoUrUDzM2B1R+tSSvzuW6ZNbPdg/Nxzup98n65LunLr3i2C/YJxsHJIX2D7dqhbF8zNH3m9t7M3\nCUkJnLh2Iv0JCwutNe/sWc58MRC/YD88CnnQ5TBccC1oLDY7cgVtukEbl6YUjlX59foG49g66UYV\nmbGx0JYkibsfJxMgTJCEOSFEnhcaFUpQxyCuxl6lT4wHf/1lS0pbGWtL3yMoWEEf8ez3Tzu7NCAk\ngJCIED6q8xGrT61OX/DOHW3yQf36md4n/n488GBGa7pZt127gqUlpafNYVmjn3DfF85bx+COJSRZ\nWQLwunl59lcshF2XHgC89frHhEZJi5x4PBtdSphL3QVCwpxJkTAnhMjz/Bv4U8elDpfvXqZWpIr1\nZ18AcK4g6C/osFy0+Lm6IFNnl07cOpFx/46jpWdLZu+fnbG1LzQUkpMzDXM+JX0YsXEE5oo5By8f\nNLYoGu+j18Ps2egS7uPTfyxzF8bTo4slha7ewbxJU7C0pPDlm3gV9kL57z8AKtduiX8DaZETj5eu\nZU5ms5ocCXNCiHzhbMxZAK4M6M3J+EgA9rqYYX8jloMRux50QT7DMiV6Dz3z35rPZxs/w8nOidBI\nrSVQ7/HQpIrt27Wfdetmep+gjkEAzD80H79gv4z3+d//oFo1rPYfYlZNcGz0GuUuJxFfyxvc3LCN\nvEK5IuUgJczh5vZU30WYJmmZM20S5oQQ+UJqmIuJj2HThlncs7Lgx1rJqIrC3fGBWjfpcyxTkpCU\ngIpK9N1o+vv0Tx/AUndp2LEDKlUCR8dMQ6PeQ4/eXc+FWxcoX6R8xkBoMMC5cwB8uBs+PlwAM+B8\nueIkubvhHB2LV2EvrYyFBZQo8dTfRZieDC1zEuZMioQ5IUS+cCbmDKDNNO1oVYN7bi5sLAvXW/ry\nSsR9bD8b/VzLlEzbNQ0Fhc8afsb0sOnpd5xI2aWBLVu0LtbHhEZDuIH9l/dTs3hNtp3fxkdrPkpz\nMuW6JUuIdrIj3FlHrenLAThU2pqbxQvhfoMHLXOlSmU60UKItGx1tgDEJsbKBAgTJGFOCJEvnI05\nSyHrQgT4BlA06gZKWU8Atr/THAWou+hfbaeFZwhya0+tZcPZDbTwbMG4puMI6hj0YIcGgLJloUIF\nbWP7q1czDY2pY+SCOgax671d1Hetz/e7vycgJEArEBqqXde0KUtq21H1QiJ4eHDdGo4QTVRRK5xi\noby1i9YyV7r0czwxYUoe2c2qqrlcK5FTJMwJIfKFMzFnKONYRpuAcPYsNhWrYaaYce3cMa0lAuC7\n755pYd0FhxcA8PErHwMPxr7tObdD20KrYkXYu1fbSmv58kxDY+qsW72HHgszC/7p8Q+Vi1Xmyy1f\nsuXcFuPODUkTJ7DD5ipmKpgfOcqO8jZYbP4Xs0OHAfC6bamFORkvJ7IoQzdrcjIkJORyrUROkTAn\nhMgXzsacpaxjWYiMhHv3sPAqh190Md4K+FPbeL5sWe0vsU6dshToJm2bxL6YfQC8vvQA7aIKoiiK\ncRkR/fL9DOs8DUaMgObN4Zdf4OBBGDVK22f1EZ/h38A/3Rg5W50tW97eglcRL9oubMuhy9rerP+V\nK86UNclcL+cKQLKjI/2+NrCnjPYXssN/l7TvKWFOZFGGljmQGa0mJEfDnKIoryqK8reiKJGKoqiK\novR+TNmfU8oMe+h4SMrxtK9FL7zyQohck5ScRHhMuNYyd0YbO4enJ02uOvDJu+7QsiXMng0XL4Kv\nb5aWKfEp6UPgsUBWn1rNPMvjzFkYxw9fdaCBriw0awZDhoCZGaxcCR99pL2CgrRtuZ5ie63CNoVZ\n230tdpZ2tJzfkvM3z7OrnA1+naBg5DUAmoZe5e1utgRXTOkW27ZNa1mRblaRRRla5kDGzZmQnG6Z\nswcOA4OATP/JoChKR8AHiMqkyG9AiTSvvtlbTSFEXhJ5O5LE5EStZS41zJUty74er/GnU7T2vnFj\nGDAAli6FBg2eeE+9h57RFUfTJbgL60on8L/OOoLnJ9KgUTfYuBF69ICzZ6FVqwdj3VK7Vp9yey23\nQm6s7b6Wq7FXafhrQ7ac28K/ZcxRB2mTI/56rTR/lbzFptjD3LM0h82bUy6UljmRNY9smZMwZzJy\nNMypqrpaVdXPVFUNBpIfVUZRFDdgGtANSHxUGSBWVdVLaV43X1CVhRB5wJnrWoAr41gGTp/Wluxw\ndcWzsCcx8TFci9VauJg4UWvN6tMnS11MNRxr4OHoAUCtDgOxiI3XxhkNGAC///5gLF7KWLd0nnJ7\nrarOVZnQdAL/3fqP6Sli7jkAACAASURBVGHT6XG9FMkzpjO5qQ1tDVH4hsOdxLvcLlFEG58H0jIn\nsizDdl4gYc6E5Kkxc4qiWAALgS9VVT32mKJdFEW5qijKEUVRJiuK4vCYskKIfC51jbmyhVNa5jw8\nwMJCW48NOH39tFbQ3l4b23byJIwe/cT77ovZx5HoI3j+n73zjq/5+v/483OT3OxFIoSQGEWMSInW\nKC66qzWSFC3VpdWpLakONTqQln47dfzotBJVpaqqzY0WpUFQeyTRSAiRvcc9vz9OboYEQSbn+Xjc\nB/ez7vl8Hrnnvs57NmnPuUUfyey/ESNgxYorSqS4FC/0eYHpA6YzKBbmLzpJcKCg55frOPvVx4SF\nw6BYKGrTCoqL5QlKzCmqibLMXd9Y1vcAzmMWcE4IsfAixywFTiBdsF2AOYAfcGtVB2uaNhGYCODh\n4UFkZGRNjpesrKwav+b1hnqGV0ZjeG7L/ltGJ8dO+Lv6l26LTo3mUOYhxrQeU+3rRMREYKFZcDz6\nOE1376bA1ZV/IyNJyU4BYM2WNeR6lFjiLC254e67aTF/Pru8vcn09a3ymtGp0cw8MBOTMPFwvDcv\nrYsH4Pt+PnS95RZ8R4zgwIwZpPn7V3n+lTJYG4xX+jJGjTqG1833sfyv5bRzaMevQRCQAKkOzjQH\nMh1t2PnPPzX62TVNY/gbbKjUxrOz0Cw4dPwQ0RmO+AO7t24lLS+vRj+joaD+9s5DCFEvLyALmFDu\n/UAgAXAvty0OmHKJ6/QGBHDjpT6zZ8+eoqYxGo01fs3rDfUMr4zG8NwiYiKEW6ibiIiJqPL9pZi3\neZ6IiIkQ94ffL9p90E4Ik0kUOtiLnSP7CCGEyCvME7pZOjHDOKPiiWlpQrRqJYSvrxB5eRe89uTv\nJwtmIva+NE6ICROEALFg45slg48QYt68K7rvi2F+BtMjpgu3UDcxf+t84RbqJjze9RDMRBx8+VEh\nQKR3vaHGP7umaQx/gw2V2nh2ju84isnrJwvxzz9CgBA//1zjn9FQuF7+9oAdohqaqiG5WQ3IZIZT\nmqYVaZpWBLQB5mmadvIi5+0AioEOdTBGhUJxGZjrtQWGB/Lc+ueq7lV6EQI8AwheGUzP7/9g5GlX\nNu/6CcusbBx9/cFoxHrBB7R2bs3RlKMVT3R2hltvhQMH4M03y7aXa8EV0i8ETdMAcHx9toyVa9OG\nF4a+XjL4y4uJqw7liwrPNswmLDCMOZvn8Er/V0jJTWH6NmvW7JQ175w6dKk0ZoXiYtha2aps1uuU\nhiTmPgW6Az3KvRKB94EhFzmvG2ABnKrtASoUistnoPdA9Do9Ngs+Yo7utopC7hJCxSwG17sk88pH\nu1nz5oMAdLDxLG2n1b5J+7KYufKMGwfW1jBnjkwoqKIF19Gso7jauNLGuQ0cPCiLA9ci5YsKl7+/\nIlMR47qP4y+PfJ7dVlKepE2bq+o1q7j+sLOyUwkQ1yl1XWfOQdO0Hpqm9Sj57NYl71sLIc4IIfaV\nfyGzWU8LIQ6XnN9O07Q3NE3rpWmat6ZpdwHLgWhgS13ei0KhqB4zI2dyOvs0US1h+BtL2f3de3JH\nNYWKo7UjRh8YOaqIN38rSXCfPx/CwgjVR2FnacfRc2WWOWOsURb+NZcPEULWoTO34IqKKk1uOJp1\nFP8W/mgREfDvv7JlVy1yflFhkIIuwDOANUfWcMtD03ks0AoBcPToVfWaVVx/2Frayt6syjJ33VHX\nlrleSOEVDdgiEx6igdnVPL8AaaXbABwGPgR+A4YKIYprfLQKheKyCd0SWtrT9OjLj7P127fxsPfg\nuJ8Xnxgc8Bs/lRyfVtUWKpPWTUJDo9+oF8jUSsRcSTutAM8A/oj9o7Q8idmNGeBZIhDvvRfuuQfO\nnIHhw+VnBQRAcDBFv28kJiuGoNNucixFRbVumauK812vj73xE1/2s4F1666416zi+sTWylZls16n\n1HWduUghhFbFa8IFjvcWQrxX7n28EGKgEKKpEMJaCNFeCPG8ECKlzm5CoVBcFHOcmzHWyFLroywL\nM9HrcCYPJrrzyvosAOziEiAwEKM3pe2zzJQXg389PQyHLTu454Z7GB+6AbccQWR7S4rfXwBGIwYf\nA6/0fwWAKRunVI7JMxplNwWgaOkS+d5ggMWL0e66kwU/FzJh7vqy2Lh6EHOVXK9xMOGgNVsnDLlg\n2zCFoipsLW2Vm/U6pSHFzCkUimsAcxxY8Mpg3rfbzZhgHT+u0HhrwW50JiiwAKHXY/rySz5+Z7i0\nopWLnTOLwYiYCD4livBwjaAFv3HD5gMwYAB9U+zZ9OjQ0nZaj934GBaaBV/v/ppJvSZVFHLBwRAe\nToGrE5FeRRQEjgCjkX0ndmBRWMwzUZD1yDjQ6+U59SDmKrheS8asX/kjfb/6/bLahikUpZY5Kyv5\nUr1ZrxuUmFMoFDWOwcfAwDYDSc9Pp9k992Pl5ILOZELoLZk2BHItTOiKiwlbko9h1a4KsXNmMThi\nxQiWN0ti6jA9D+7IBxcXOHAA/cofGfzR2tL4twNnD6C3kGLs438+LrXqlbbgGjwYfe8+3IwXI0YU\n8PWXT/PfZ3MAyNBD02/CZPuupk3Bza1enlcpV9k2THF9U2qZA+lqVZa56wYl5hQKRY1jjDWy7ug6\n9Do9Yt06REICtGmDpY0dJ3xcuDu4iCIrCyxy82HqVCnkzp1DpKZy5OXH+eWLqWQUZNAkB0K32qNZ\nWUFaWsUYMoMBY5C04i0dtRQnayc6u3cudfFWaMEVEIDD0Th2eOn4Rn+Q2w4WUmCh4VQA2htvYNrw\nK2ecG0AN9RpoG6a4fim1zIF0tSoxd92gxJxCoahRzAH97nbuvJjnz3cri9GA3bOfYvfCGXz+XRoA\nC/qUnNCxI4Wb/4SgIIrdmlDw1f8xa/5OXthhxY51LXE5mYIoKpSlRs6LITPHmw3vNJypfaeyNX4r\nMwfOJCrxPEtWQACYTHQ4kc2Q/yzQmeD9mwUFljrid0WSaVmMjaNr3TwghaKWUJa56xcl5hQKRY0S\nlRjFwrsWEp8Rzx1pTbEcdh8mSwsW2xzitoQ5RM6bxAP7dTy8o5h5g/WkJ8Rw1/Bs+j0CX97WFGdX\nD+wKYcHPhfjsT8BSwPS77TDOerhSDFn5eLPJN0+mmX0zVh5cydS+UyuMaYtHAQABiTB87JvogHUd\n4K9O9tiH/YhzHjiNf7xOn5NCUdPYWpazzCkxd12hxJxCoagWkU/dRfTSBRW2RS9dQORTd1XYFtIv\nhEKTLCFi/9osiI1FF9CbVi07ERYYRlCXIMYfd2B0sMa0AQUMH1nAih8tCB3wJk/8coZli19k8z8/\nyFIigDZuHEM+/Fla2y4SQ+agd+D1W14nMi6SjTEbK+zbUhRDShM7eifCDQeSEHo9D6S3ZqtrJk1y\nSor0du6sui0oGjWlHSBAibnrDCXmFApFtXDuPxSviVOI/v49MJmIXroAr4lTcO4/tNKxxjgjztbO\n+Dt0kMJr0KAyK1pUFPofVuM9/GEAfEY8TJM1G+mXpEen6QjpF0L/LFfYvBmmT4f16zHESZEIXDSG\nbGLPibRxbsOrf7xq7t0MyHMPtXWk72k9+i1/k+7XkfXuaUyOtibfPAumpaluC4pGTSXLnMpmvW5Q\nYk6hUFyQ8jXf/Me+SOxnc+k8YSrZtpa0f+Ql4j9/F/+xL1Y6LzIukgFtBmCxbbssxjtoUNnOkBCM\n3rDmyBqmD5jO2iNrMXpTJtDMJUXCwmD27Msqz2Ftac2sQbPYeWonPxz8oXS7EAKjWyZtzhZg2rmD\nxY7HeH7aao7PnoOVhRUFOih4epLqtqBo1NhZ2VEsiiksLlQJEFVQfj4zU9oxppGjxJxCobgg5Wu+\nLdm7hGGnF5ClB/sCgWM+OM19n1Nbf6twTkJGAkdTjmLwNkBkJFhaQt++pfurajZfmoEKV12e48Hu\nD+Lr7svrEa9TZCoCIO3N10gtlj9sumITQ8bPxBAHjocOoXv6afQm2HFfLyXkFI0aWytZLDi3KPey\n3axmoVM+nMIsdKoKp2iMmOezjcc3klWQVbljTCNGiTmFQgFUvWoFGNlpJCPCRvDgjw/SdW8STXMh\n5oZmZOrB7UgC7rfczvphnfll3M1EL11A0oyXGBQr68VlrVxKelMHKcRKYtEu1Gy+NAP1KstzWOgs\neMvwFofPHeab3d8A8G9ra6ZtLjlAp8PPrQsEByMsLOD772H6dPr+vEcV51U0amwtS8Rc4eWLObPQ\nOevrjdfEKYR//BTBK4PxiY67YDhFY8M819y7/F68FngRFB5UsWNMI0aJOYVCAVRswwVlFrTRXUfT\ns0VPBsVC2ErQgLaTZ3Lsq/kUW2pE92jB7esOcdPK7bR7+CUi/vuL8JUaDvM/xv7oCYrb+VSIRbtQ\ns/nSmLgaYHin4fRu2ZuZm2aSV5THr175jL5fh7DQgYcHTJgAr7xC66VLr8idq1A0RMyWuZzCnMsW\ncwYfAytGreCB1P9j4oPO3DplIcdmpzP45c+I/+K9KsMpGiMGHwOtnFrx+B9pjEluXnEuasQJUErM\nKRQKoGzVet/y+/D9xLfUFQqwNX4rNyfqmDvEWh7s44P/2Bc58eV7ZN/Ug6w//yDfuxVOBfDc6kQy\nbS1p994iNKDJ/pg6j0XTNI05Q+ZwMuMkC6MWsiNxByk3+6GNfwhOnZLFh4uKODBjhuq2oLhmKLXM\nmd2sl5kA0btVbwpNhfzYIo3tLcE5q5C43h2vGSEHcpEalxZHVEt445P9GN+fDMXFYDRSEDiC5bbH\n63uIV4QScwqFopT+rfujoXEw+SAT/CYAELwymBua3sDmMX2ZMOgFALZZJQEyKWLQp7/g1H8wnvtP\nULjwE4SlDp/kQnIcbeRFn322XmLRdiTu4MYWN/LO5neISozigWRPCn5aVdbAPiCANH//iiepbguK\nRkxpzFx5N2u5rO5Lsf7oegAmZXSif7zc1nnLoUoliRorZm+Dl5MXpwM68cBIGPjiByQP7kNB4AiC\nAwUe94yu72FeEUrMKRSKUmZumklGQQZTN8Oh8IUs37ecsMAwknOSGZbggO+vOxCaxhbtZOWTdTr2\nOeWRaSXYPaQrtpl5nBpxK3z2Wb24LgM8A4hJjSE5J5keB9N44t0IggMF+W+8VupSdYmOrvNxKRS1\nRQXLnK2ttDgVFlbrXGOskSd+foJBsTD7s8McuLk9AEne7rIk0TUg6MzxuiZhonfL3tx423h0gNuf\nUXzoX8Dz01Y32vg5JeYUCgUAETERhG4JxcXGhaiWsCzcRNLPy8ktyqXjv6d45r0/QadD8/TkpcGv\nVTrfXHcu9fkn6bHnNMdfnYTVb79zdGJQvcSiGXwMrApehd5CT0ACjAnWlU3WJS5Vx0OH6nRMCkVt\nUskyB9WOm4tKjOLtwW8TkAAb3nmEdu17AeB+Kp34z94lffPvtTLmusQcr5uen46ztTNvFQ0AIMsK\nJu3QMMTV7/iuBiXmFIrrHHMW64r9KygyFTF3yFy2ttPz6sR2hC83YTvtdcLCYfv7L8kYnLZtq7xO\n+ubfif/iPTo4e0NYGB3e/pT4L94jITWu3mLRDD4GJvWaxLv9ocfoFyquug0G4seMqfMxKRS1RaWY\nOai2mAvpF8INTW/g3f7gNXw8tvkmAByyCvDvdTeDPv2lVsZc1wghyMjPoMeBVMTkyQA4FMILd+ko\nCBzRaBOglJhTKK5zzFmse5L20My+Gd4u3piEidgmGlZZORhWRbOwFzjfMRxiY8HHp8rrDPr0Fxko\nXa60iDmmrr5i0YyxRpb8u4TpA6bz2c7Pqiy9olBcK1yNZQ4gIz8DACdrJ6xzC8m1LNmxbVtNDrNe\nySrIwiRMOP97hLf7m0q3P3/zZIIDBcc3LK/H0V05SswpFNc5Bh8D82+bz/aE7XR268yDPz7IQO+B\nTP72KJhM5DjZMmkHdNx5AhISLijmGhqXLE6sUFxjVGmZu4yM1vJiTpedzYEWFuTa6a8pMWe+x6V3\ntiLwriml27vEZPL8tNX8MKxdfQ3tqlBiTqFQsP/MfkI2gxa5iUm9JvHMEVcGH8xD2Npgl5HL1EAn\n7Mc/KjPjLuBmbWhcsjixQnGNUcEyZyv/fzmWucyCTECKOTIzybOz5kh712tKzKXnpwMQ3CWYrtZe\nAMS4gvj77xqvd1mXKDGnuC64lnvyXS2FxYV8uetLor0s+Xm1HXuXf0Dv8C0UamCysADAw8kTXn5Z\nntBILHN1UZxYoWhI2FlJa9yVxMxBmdXKUe8IWVkU29mwx9sG/v0XsrJqfLz1QXqeFHPONs6QLv+/\noR0QHQ15efU4sqtDiTnFdUGF7gahstdghZ58TzwhX+VpxNXAL4d5W+aRmpfKgAkzsP/xZ8JXCJof\nOUWRBUTMe5J8CxgUbwFOTvKERmKZUyiuNyq184LLFnN6Cz3WltaQlUXL1GLOaXlgMsGOHfKgRj4v\nmi1zztZSzAlNY2Nb0AoLpaBrpFhe+hDQNM0WCAFGAW0BAcQA4cB8IcTllZlWKOoYs4tt+IrhPJjc\nklmzD/HKtJLG0UYjLF8Omkb0wI5sbFNESEGALKcRFla/A68Dlv67FFcbV6b1nwY6S6x8u8Lff/NZ\nAJzwKcTWE7oey4AbYsHaGlq0qO8hKxSKKrCysMJCs7gqy5yTdcmiLSuLTG8nHtpYUlNy2zYZZtHI\n50WzZc7J2gnS0zHprdBEgdy5bRv06SN/E6KiGlUB8UuKOU3TLIEI4EbgV2Adsj2jL/AGcKemaQOF\nEEW1OVCF4mrp3bI3JpOJTx0P4vvyMF6YsY4tX22gIM0e/erVRJ+KpvNDL+HbpCkUFMOqVfXSuaAu\nOZV5iiPnjjCl7xQsdZZyEtu5E9zcmPBvKs/+Es6W1tBneyJ4H4A2bUCnDPoKRUPF1sqWm5f+CYbO\ncoNZzFVDoGQWZFYQcxnebQnuksDG73RoX38N8+fXeWu+msbsSi51szo58dm6ZLJdHbDftk0+p0Yo\nWKszK08E2gM3CiHuE0K8IoSYJoS4Fynwbig5RqFo0Dy17imyCmXcx+GdG9BMJvrHFPFBj3xa7nqQ\new7NQG/SYX3mHKSlkbDof/xvw6x6HnXt8u2ebykWxTzi/0jZJOblBTfdxIcv9uN/i0+RoQeLomLY\nuFG5WBWKBo6tpS1H27rAc8/JDbm5Zd/tgICLnpuRnyHj5YqLITcXS2cX/vA2YWrrA4cPy57GjVjI\nQWU3q655Cx4cbYU+Kxd+/bVMyDWy+6yOmAsE3hZC7D9/hxBiHzCn5BiFosFhTnwI2x/Gt3u/ZWCb\ngbR39mGasQhzx8In/i7khn8TeSoyC53JBGPHUmytp/mSNUwa/xGsXHlZ/Q0bC0IIFu9eTP/W/bmh\n6Q1y1R4WBsnJ4OODadBAgoNAX1xyQkFBo0l+UCiuV2ytbNnj2wS+/lpu+OGHaguUUjdrdjYAeucm\nDIoFLf4kWFjInsaNtKiumfS8dHSaDge9A6Snozk7E9OjDfu6ukNGBgwa1OiEHFRPzHVBulkvxO9A\n15oZjkJRs5gTH068+jRD4nQ8fuPjPLQ2Hs8MEwm33YxJg7XtivlphUbIFjhjB0OGJjLyIWtMTg5Y\nO7lCUBD4+sKyZTLw1zyZmQOBG2lA8Jb4LRw5d4RH/R+VG0JCoEcPSE8ngliEEAQkQHQnJ+jUCQCj\ndqLR3q9CcT1ga2krY+buvltu+P33alvUSsVcSeZq03N5hIXD6Qfulda6Tz6pl9Z8NUl6fjpO1k5o\nmibdrM7ODEtwwOdoMnh6SvH7/ff1PczLpjpizhU4e5H9ZwGXmhmOQlFzmISJguICOjTpwC+uyYSv\n1BH+yVNM3ufAWVtw2baHaE8dNyVA/r13YYHG0u4Q8V8kazwzeeaxFvxh8Ob0rBAKY44hxo6FzZvl\nZLZgAQWBI4iI/6ta7ouGyKLoRTjqHQnyDSrbGBsLgLtvAJ9EfUJUS/hqaQ7JzZ0B8LZyb7T3q1Bc\nD9ha2cps1k2bQNOgV69qW9Qy8zMriDmn1GyCg+C/Pl3kAc2b11trvpqiQpJHejrk5TFz4UEmjnOB\nrVvB3h4mTJAu10ZEdcScBXCx5AZTyTEKRZ1TVf24NYfWcM/Se+j4cUf+mHQHzaMOUjSgPyNHFbF8\nST4OZ9JwKoBnJrgT3t+V9qmQ62iHziRY18OePq36YG9lz19tLRna8ndaiFD6T23Kdi8N1q5FpKQg\nXnqJGMtM+i/5q2xyO3+ybMAWrIz8DML2hzG662js9fZlO0rEXLeb72XZqGVE+sCUR72w/ns7AD7f\nrmmU8SQKxfWCraUtN+xNkIuuFi2gQwf5na2GRa00Zq5EzBUFBxLpA/HNrOUBx47VW2u+miI9P13G\ny4EUc9nZhL8RyA/NUyjyagkrVkgr5Btv1O9AL5PqiDkN+F7TtDVVvYBva3mMCsUFKV8/btepXdy1\n5C7uW3Ef646uw8Peg9tHv8byMEHn33ez4FR3rLPzAfixi45mB/7jEOco1mm0WhzOSWeN0H6z2Hpu\nOGvHrOVszlnCAsP49K5Pce7YnX6PwpKuoJlMZFhDpzMm0m/0lZNbgCxlEr10gSxEXM2A4/pixb4V\n5BTmlLlYzZSIOXx8uL397Qz1Gcpi11h2BvaX2595Rgk5haIBYl7Y2lrZ0vbYOQgLI8PNkZjjO+V3\nthoWtfPdrE5NPQGIczKBpSUcP17r91HbpOely0xWIaSYGzSIooG3YBImTmWegrvugldflc/KHHfY\nCKiOmPsGSATOXeCViBJ0inrC4GNgxagV3LHkDnp+0ZP1x9ZzT4d72P3EbjY/shnd4CGEDC7i82VZ\n+P++D4Afu+u5O05PoQ6+XAv/NtehE6Dv0x//F+ZCQEBpXbrYtFgmBUzit3G/kXbTau6I1fFtd3Aq\ngCJ7W1yMfxO9dAEYDOx953m6jnuJcfN/b/AZUYuiF+Hr7kvvlr0r7oiNBRcXcHHBGGtkd9JuFjuO\no+vqLcQ9O+6aCIBWKK5FzAvb7IJsFt3aFKM37MiLwS1PkwdcwqJWZCoityi3tJUXgI2LGzaWNpzJ\nT5Flia4FMWe2zOXmQmEhODvj5SzbesVnxMuDZs2Sz+upp2T3i0bAJcWcEOLh6rzqYrAKRVXYWtlS\nUCyLPk7tO5W1Y9fi19wPgB3x25l9tBWatTWYTGjjxuGy+lfWzH6A1zZrvNMfOqfKKIFm2/6tIMAq\ntH4yGrF+YDzvDbTizmPw2p02aCYTViZo89hLhLwawC/hc7AyQYsfNzboFP79Z/azPWE7j/o/KoOA\nyxMbCz4+pU3qf2v5Cg/PWU/8F+8R4LWe6A9eafQB0ArFtYh5ARp9OpoTaScIXhlMl4634JRTfOmT\nkfFyQAXLnOboiLudO8m5ydC+vXSzNnLS89JLCwYDUsw5lYi59BIxZ2kJS5fKhe2oUTLLtYFzxdU/\nNU1rrWmar1bp10ChqB0u1F/18bWPAxDSN4Svdn9V4Zipu+1w2ntYmtTHjYP16zHEgee9D/DYg47c\n3mYwH/cpCfl89tkLCrDjG5YTHCiY0OVBHh5rx64xA5k03AqAnzppWG/bweTNhfLgDh0atAVrcfRi\nrHRWjOs+rvLOEjFnblLvf7IIwsLwH/siYYFhbGxT1OgDoBWKaxWDjwFfN1/O5Z5jUq9JeHh1gpSU\nap1b2pfVuixmDgcH3OzcOJt9Ftq1k2KukZdpysjPKK0xB1RtmQOZ7LF8OcTEwGOPNfj7rk4HiPuB\nJkKIheW2LaSsUPAhTdNuE0Ik1NIYFQqgzI2w7dxI2t0+GqM3DF8xnMz8TJ7L7sa86KbcERRG8Mpg\nwgLDMFi0k83hraxg/XoYPBiMRgoCR/BBoOD5aasxxMHgwBG8NwSe++RD9AZDlYLuh2HteN5zNR19\nDLj/VMyqg6v4oNX9HGq5hH4n8ijQwCa3kAJXJ/QpKRAe3iBdrQXFBXy791vu7Xgv7vbuFXcKAXFx\ncPfdZRbJkLKxG3wMZY3rG9A9KRQKiTHWyOFzh9Fb6Fm4YyGP6O7AOzVV9la9ROcWs5iTlrmSn/MS\nMZeckwztBksBlJICTZvW9q3UGun56WXdHwCcnXG2dsZB71BmmTMzYADcdpucz/v3LyvE3ADbfVXH\nMvcsMmMVAE3ThgJPIFt5BZVcY3qtjE6hKIfBx8DyUct5Lvk7skbczQdzhzOg9QAGxgrm/V9chVi3\nqIR/4OmnZVbSN99IIQdgMLBq5v3Mdx2NIQ4IDka/8kd6frmOVTPvv6ALMaRfCAYfA6FbQvF18yUj\nP4ONTVNpeTaPG1Lg4d2Q4uVOUVYGIiVF9vdrgBastYfXkpyTXDnxASApCfLyVGFghaIRYg6NCPQN\npLC4kOWjlvN/cT/KRVo13ISZBZXdrDg44G7vztmcs9LNCo06bi6vKI+C4oJKljlN0/By8qpomTPz\n0kug18MLL8jerQ00ua06Yq4jsL3c+/uA34QQbwshVgEvAbfVxuAUivOxsbThF69chg3PZdnSAmZ8\nup/wcBDLl1eMdTvdDn7+GebOhTFjKlxj9LOf027u52UdDwzS4jT62c8vKcACPAMI3RqKi40Lz+ev\nZswoEIDeBPapWSQ98SCaEHDkSINM4V8UvYiWji25rV0VX9lymawKhaJxYQ6N8PPwQyDo3bI3Dwx8\nRu6shqu1omUuS3o09HrcbM2WuXbywEYs5tLzSlp5lbPMLY75AWOsES/nMjFnjDXKqgQAQ4ZIyxzA\nffc1SI8LVE/MOQDl/xL6UrEjxH6geU0OSqG4EGsPrwUg0geO2efTa0ss2wd3ZFsH27IvX1qajH+7\n8cYys3hVhIRU/kJeQoCZLX95RXmYhInffa3J6CEbWkfc0wWfx6bIAw8evOJ7rGnMsYYnM06y4fgG\nJvSYwJ8n/ix7XmaUmFMoGi1m74G5IG5Gfgadb+grd16GmCutM+foCIC7vTsZ+Rnkt24pD2zESRAV\nBGuJmOvU9iaChS1xjQAAIABJREFUVwZjoVkQnx5fauEM8Cxnebv3XmmJO3OmwSa3VUfMnUS29ELT\nNCegG7Cl3P6mQFbND02hqIgx1shHUR8BcNsJKzqelQGpQzYe54O5w8u+fNOmyS/dl1/KrKQaxuBj\nYEKPCQC8a3kXzifPsvielvT5eQ+cOiWrrjcgMWeONZxhnIFJmOjs1rnyZAVlYq5Nm7ofpEKhqBHK\nizmaNJEbr8Qy5+AAgJudGwDnyIGWLRu3ZS6/xDJXzs3at9udhAWGsenEJpKyk8pirn3KCTajsaxE\nSQNNbquOmAsHPtQ07RHg/4BTwLZy+3sBh2phbApFBaISo+jr1Zf7zzRj7WpbUm3l9tO2RYSt1GQM\n3ObN8PnnMHmytMzVAsZYIysPrGSx4zjGvLWa6A9e4e/H72TCWFvEuHGy6vqBA1WfXL63a+kFa7dT\nhNma+PWer2nj3IbJGyZXnqxAijkPD7Czq7WxKBSK2qVKMZeaesnzKpUmKRFz7nYyUarU1dqYxdz5\nblZNA0dHDD4GbmsrQ09GdR5VWcgFB8OjJXHGn3/eIMszVUfMvQn8DcxHWuUeFEKUL1wzBlhXC2NT\nKCoQ0i+EnMIcDMkO6JeH456rccIZvNNA/+Y78PffMHGitCzNmlUrYzCb4MMCw3i4qCvxX7zHbQlz\nsLaw5ifPDFK/+QycnC5smSvpFFE6EdRRMG2P5j0wCRMn0k8wqdekykIOSsuSKBSKxksFMefqKjde\nhmXOQe9QpWWuQnmSRkoly5yjI+h0GGONbDqxCYCl/y6tWALLHFt9883yfZcuDTK5rTpFg3OFEOOF\nEK5CiM5CiL/O228QQsyrvSEqFGXEpMbwz9hB/G19Fp1JkOvbgTwLSNjyq8xcPXgQnngCPv20Vj7f\nHGRs8JGxdeb6a/km2SZsd2dXGDZMJkAUVdHS2NxWZ+RIudKro2DaFftXABDkG8TCHQsrTlZma2F5\nMdeA+8oqFIoLc1lirpynICM/A3sreyw2/QmHDpVZ5kpKGLl9vFiWNzl9GrKz5fmNbJ6oZJlzdi5d\noK8IXIHeQs9dHe4qbREJlMVWl3+WDTC57ZJiTtO0TE3TMqp4xWua9rumabfXxUAVipzCHE5nnQYg\ndOlTAHQKfBK9hRVNwtZieutN+SVbsKDWLF3mIOPyGHwMzB40G4APtn3AQTegoEAWm+S8zKjiYtiy\nRSZpLF5cJ8G0xlgjIRvlxPPGwDcICwyrOFmZrYUnTkgx10BT7xUKxaWpIOZsbGTYxIXcrObv/saN\n5GakcEe8tXxvYVHJMhfTvqlceAIcPlxxnqiH8JEroVIChLNz6QL91na34uvuS2peqixvlXie5c0s\n5qrhsq4PquNmfQZZa+7813vIvqxrNE0bVmsjVChKiE2VAfq5Rbm80+4JuXHECHTvzMG2CExCwJ49\n9ZI23tyhOU1sm2ASJibHfyE3HjxYMTMqIQGGDoXp02Wshl5fJ8G0UYlRBPoGAtDWtW1ZLT7zZGUw\nyEnXZIK9exts6r1Cobg0FcQcyLi5C1nmzJ6Ce+/l7ceX8cX36WWCzcGB0C2h7E3ai4bGHt8mcqEM\niJ494dZboW9fOHkSPD3rJXzkcjG7WcuLufILdD8PP/Ym7a3YytHMZcQf1gfVcbN+c4HXB0KI8cA0\n4NXaH6rieicmVVq6nr/peTpn20qTf6tWspijry+WhUWyUHA9iBBN0+ji3oWUvBRee+w7AL5ZPq0s\nM2p/Nvj5ybg+Bwd4+GFpvfv++1oPpg3pF4JJmGjp2BI7K5ncUGmy2rhRZv7+/HODTb1XKBSXxtFa\nlhQpFXOurhePmevZE4qKcE3PZ9WAZvK7XxIzF+AZwJgfxuCgd+Bs9lmMg9sSdqMeDaB1azlvjR8v\nWyUWFsIdd8hXA10QpuelY29lj6XOslTMlcfPw4/TWac5k32m8smXEX9YH1xxb9ZyrAM61cB1FIqL\nYhZzbV3byrZTrVrJwpabNslSJNOn11vaeOiWUFxtXBkSvgu/xGISnTQ4eIiuTh0wTPlYxtF5ecn6\nd2vWwE03yRPrKJj2WMox2jdpX/XOnTth2TL5LOvxGSoUiqvHUmeJnZVdRcvcxaxJb75ZGt8btOms\n/O6XiDmzFT+nMAdjnJEP5o1geJytnCcyM2HVKmnN/+wzGD5cunQ3bIARIxqckINyrbxAijkXlwr7\n/Zr7AbDn9J7KJ5uPbayWuWpgA+TVwHUUiosSkxqDg172CiQuDry9y8z5YWEwe7b8tx7SxgM8A4iI\njeAvjzyKgwI5ZSfoGw9fvvq3nPBGjJBWuXfflZOce0lf1OTkOgmmvaiYmzhRun1XrKjXZ6hQKGoG\nJ2un6rlZjUb43/9K3y4Z00V+97OzS2PmDD4GOrp1xCPqIN+vKES/8seyeWLMGDmHPfEEPPSQnEdA\nLg4b4PyRkZ9R6oa+kGUOYE9SFWLOykpmv17DYu4xYHcNXEehuCjHU4/TzrUdmqaViblyLbmAshiQ\nOk4bN/gYmG2YTaQPjAosxjcZOqRA2xR4+3ZbjPOflcHIZtxkUDFnz9b62DLzM0nKTqpazP32G+za\nJV2rw0pCX+vpGSoUiprBydqJjIJquFnXr5dWuVGjAChwbyJ7WUOpmDPGGknMTCQgAYIDBUbvknPL\nzxPmRXV4ONjby3i6BrggTM9Pl2VJhKhSzDW1a0pLx5ZVizm4tMu6HqlONuuHF3h9o2nav8CjwOu1\nP1TF9U5Maox0sRYWyqBbb+8raslVW0zoMQEdOiJ9YP9t/gB8PcCJvgvXVc6MMlvm6kDMHU+VRT4r\niTmTCV5+WT7HksDmUhpg6r1CoagelSxzF7Im6fUy9njaNACap5vKiq07OJQmcK0KXsWWMX3Z3dm1\nYia8eZ4wL6qHDJGxwefONcgFYXpeiZs1L0/+jpwn5gC6e3Sv2s0KUsw1Ystctwu8XID1QFchxM5a\nG6FCAZiEidi0WCnmTp6UQsTbu76HVYHdp3fjauvKYsdxeG/azfrRAQzbkUHnfacrZ0bVoZg7liKL\nfFYSc8uWwe7d8NZbYG1d6+NQKBR1g1nMhW4J5ThpkJsrX5QrlVRcLK1wt92G6NGDIh14pBXKeDkA\nR8cKdTXbubbjVNYpngl4pnRxWnqt8ovqHj3kvDJwYINbEJZa5kpaeVUl5vw8/DiYfJCC4oLKF7hU\n/GE9Up1sVsMFXvcJIUKEELF1MVDF9c3prNPkFeWVJT9AgxJz5hXsxlav8vCc9cR/8R4P9DhGcBC4\nPPREZXeDq6tcEScn1/rYzGKunWu7so35+fD66+DvL+NeFArFNYNZzAV4BvBpzDIAkuIPVSyVFBEh\nF8YPP0yeKOSUA7il5JWJOQeHCmU7xvuNR6fpCNsfRki/kKob0oMUcxkZZfN0AyI9rxpirrkfRaYi\nDp6tootPY3azKhQNgUqZrNCgxJx5Bet/sgjCwvAf+yLhQeH808GOD17sW9ndoNNB06Z1ZpnzsPco\nLVkAyIzVuDiYN0+ORaFQXDOYxZzBx8DDhpcAGPLBjYxYMaKsg83XX8sMzXvvJbMgkwRHcEnJqSDm\nyjO07VCeDniaA8kHGL1ydNUN6UEuEAGio2v5Li+f0gSIS1jm4AJJEI3czapQ1DuVxJy5xlwDoXQF\nW87dMKTtEO7ocAefOR6u2t3g7l5nYq6CizU9XbpWhw6VgcoKheKawklfFjPXtdMtALjmQmFxoZwL\n0tNllv2YMWBjQ0Z+BiedwDE544JiDmDu0Ll42HuwYv8KhncaXnWP5y5dZAeJ3Q0rL7LIVER2YXZZ\nKy+oUsx1aNoBG0ubquPmGrObVaGoT0K3hGKMNRKTGoOGRhvnNpzat40MN0eZKt7AGdRmEHFpccSl\nxVXe6e5eZ27WCmIuNFQGKM9TLZUVimsRs2VOCEFUrkyA8si3JLcol6HfDaVw6fcyCeDhhwFpsUpw\nAvuk1IuKue0nt1NoKsTZ2plFuxaxZO+Syh9uawudOjU4MWcWt5dys1rqLOnarOuFLXN5eaXxhw0J\nJeYUDZoAzwCCVwazNX4rrZxasTV+K7HREZjatK7voVWLQd6DANgUt6nyTje3WrfM5RTmkJCZUCbm\nEhLg/fflityctaZQKK4pnKydKDIVseHYBp7c+goAXa1aMtswmyPnjnB0wesIX1/o1QuQ5YtOOoFV\ndi4kJsqLnCfmzDFyK4NWsu2xbTjoHXho9UOsPrS68gD8/RuMm9VsEEjPkwLO2caZQ8e2y51ViDmA\n7s26sydpD0KIijsacH9WJeYUDRqDj4Flo5YRERvBkxGZfDB3ODfmN8GlUw95QANs5lyeLs260NS2\nKZEnIivvrEU3a3mL5tTN0PdYPsZYI3smjZR1pe6+u0E/N4VCceWYC+Nuid/C+6Nl3biWRba8PuB1\npja9F99jaWwb2rG0yG9GfgYJ5pDaw4flv+eJufKZrZ3cOrH+gfXoNB3PrX+OnMKcigPo0UMuHOsg\njORSmA0Cf8T8AcB/6f+xdPOncucFxJxfcz+Sc5I5nXW64o4G3J9ViTlFg8MsRMxExkVSLIrZ2DSN\nJSsKsTl1tmL3hwbWzLk8Ok3HQO+BFe6nFHd3mRlVXFzjn2uewH48+CNRLaHPS//jh5eH0X3dDrjn\nHpg8uUE/N4VCceWYxdx4v/EM6H4PRTpoXqAHYO7JThTrNO63Xsvf8X8DZW5WAA4dkv+eJ+bKZ7YC\n9Gvdj7CgMBIyExi9cjRFJtkSrMIi0exqrcdFt7kl2ZSNU5i6GaK+D+WRtqOkkHV0rHJsF0yCaMD9\nWZWYUzQ4zELEGGtk3ZF1vP3X2wB43TeOF++0kNW7d+9usM2cz8fgbeBE+onKcXPu7rJeXi2s8swT\n2Lwt84j0gTHBOj5cnY9maQl//tkonptCobgyzGIuIz8DE4I0G3DLs4TiYnTffU/xbbeib9WGUWGj\nOJV5qjQBApCWOQuLatWeHN5pOJ/c9Qlrj6zlqXVPSbdkQADMmSMP2L27QSy6DT4Gbmp5E1EtYVmY\nCe+TWVLIbdpU5di6e3QHqujRqtysCkX1MQuRwLBARqwYAcDcIXP5dsS3TG07Th60dq1sQdUIBIk5\nbi4yLrLijlpu6WXwMdCuiawt5z/yaXSFRVBQAE891Siem0KhuDLKi7mU3BRSbKBJnoDff4fERPSP\nPs6P9/9Ick4yt353K+dyz5FodrMmJJBna1XWZ/USPNnrSQZ7D+bLXV/y5p9vyrklPByh0zi7cH6D\nWHQbY41sOrGJnR0deXC0FcXrfpY7LjA2V1tXWju3rmyZU25WheLy6OPVB72lnkJTIcM7Defl/i9D\nURHtF63CZGnB1glDZK20Btb7ryp83X1xs3OrLOZquQtEREwE+5L20dmtM0mLP5Qb77uv0Tw3hUJx\nZZQXc0lZSaTYglN2MXz1lRQkw4bRzaMbL/d7mf1n9zN381wK9RYUupQouioyWS/Ga7e8hrWFNTMi\nZ7Bo1yKM3nCsqYZ7bFK9L7rNiRtudm4MaTuE56etJs2ySBY2vsjY/Dz82Ju0t+JG5WZVKC6PwLBA\nTmed5v4u97P5v80y5uyttyApCd0bM+j71e9yRdUAmzmfj07TMbDNQIxxxorZUWYxVwvlSYyxRgLD\nAzFh4j2re/jwF7l91wNDGs1zUygUV0YFMZctxZxzwjlYvRrGji11ob45+E2CfIPILszGUmfJYVuZ\nyGDj3PSyPm9w28GsHbMWK50Vj699nP/NvY+2WTJGj08/rde5JioxikXDFpGQmUBvz94Ytp2maY4g\ntneHiy5su3t051DyIfKK8so2OjtLi6WyzCkUlyZkYwjrjq5jbNexLI+5kd9avkLwymBy/+8zaNEC\n+vSRAasGQ4Ns5lwVg7wH8V/6fxXj5mrRzRqVGEWQbxAA/U5bYfnIowD8rU9qVM9NoVBcPmYx1+7/\nfkBERJBqC3YnEmQbv65dKwT8Lx21FB8XH/KL89F7+ciNjo5VXfai3NruVsb7jWdgrOC7ZflYvPmW\n3BESUq+Lx5B+Idha2QJw239WMHEiAD5f/3TRha2fhx/FopgDZw+UbdTpZNcMJeYUiouz+/Ru3t/2\nPv7N/fl2xLcQEID/83PYnPcAtglJMHKkrJFmDlg1GBpcM+eqMHhLU34FV6tZzK1aVXkyucrsr5B+\nIRxNOUq3Zt1wfuNtsLQEW1uevvfNkgE1juemUCguH7OYi23vRp8p/8O2sGSHj4/syVwu4P+vE3+R\nWZDJy/1eZhvxcuNlullBegN+OvwTvRM0goPgz6E3SCtWfn69Lx6jEuVn+8Zly7IprVvLwsYXWdj6\nNS/JaK0qCUK5WRWKC5OWl0ZgWCDN7Jvx64O/YqGzKP2ydXxrIej1sHx5vQfTXgmlcXPl681ZW4OT\nk5w4y68OayD7KzM/k83/beaO9nfIDTEx0LZttYOaFQpF48Xa0hq9hZ59XdxZ8tq93H68ZMeZMxXm\nT3M8WVhgGHOHzuWWPqMBSNblXeDKVVP+OhsDe5B8UzdG/fYI2e1awz//1PviMSoxig5NOmAb8irs\n2QN33FE2F15gbO1c22FnZVd1eRJlmVMoqkYIwYTVEziRfoLwoHCa2Tcr25mbK7MwG3EmpqZpDPIe\nRGRcZOW4ORubMnP/o4/WSPaXMc5IoamwsphTKBTXBeaWXn93sOHPjjZy48SJFeaV8oWAAXy69gfg\nrHZ57arKX6dni57EpsWyYtQKYjq4wfbtspxUPfJPwj/0btkb/v4bMjOlmLsEFjoLujXrVnVGqxJz\nCkXVvLv1XX46/BPv3foefZdtLrNSpabC+PEyVsFgaNSZmIPayLi52LTYso3mll4Gg2xQvXgxTJhw\n1YL112O/Ym9lTz+vfnIiPX5ciTmF4jrCydqJjIIMmv2zn5vjiuDll+G77yrMnxUKAZt7NgOdfUq8\nAtUM9yh/nZ6ePUnJTcHH1Yduwx6V81tcXI3e2+WQ8ebr3LA3kQDPANiwQdbQs7Ss1n119+jO3qS9\nFRfgys2qUJRRvsvD7rTdvPLHKwzyHiQzhwICytyO998vJxhbW5g+vVFnYponuwpxc+7uMpvVaIQt\nW+S2RYuu6v6EEKw/tp7BPoOxtrSWk2l2thJzCsV1hJO1E212xfDyh7uY99yNMHfuxefPgACYN0/+\n38HhisM9ennKfq87T+2Em26SG//552pu5Yow/8b829qGsHAYckJH5ppwzni7wyOPVOu+/Dz8SMlN\nISEzoWyjcrMqFGWYuzyE7w9n9oHZtHRsyb9J/0pTuDkodcQI2LhRxpatXSu3N+JMzM5unXG3c68s\n5uLjIShI9kwFmDbtqgTr0ZSjxKXFVXSxghJzCsV1hJO1E15HTvPEOBeSevvKjRebPw0G+Ppr+f8d\nO6443KNbs25Y6azYmbgTunWTYSTbt1/dzVwB5t+YTx0OMiZYxw1PvobjvqM0TUyr9n1VmQRhdrPW\ns+v4fJSYU9QLBh8Dy0YtY+yqsWQUZpBVkEV4UHiZyd9ggHayewEvvVTxi9dIMzHPj5sL3RLKf1Y5\ncmJ4/PHS435Ji7qkYA3dEkp0anSFbcZYI6FbQvn12K8AlcWc+XkqFIprHidrJz4f7MzqFul42HuU\n7bjY/DlsmCz9tHnzFRf7tba0pmuzruw4tQPef1/OO+Utc1eRqX9+324om/fOx9xJKPxAOPu6uLOu\ndT4AFvePrvZ9lbb1Kh835+oqF95ZWVd0D7VFnYo5TdMGaJq2RtO0BE3ThKZpEy5y7Bclx0w5b7u1\npmkfaZqWrGladsn1WtX64BU1jpXOiiJTEcUU80zvZyo0ccZolH39OnSAL75olG7VqhjkPYj4jHhi\nUmMI8Axg8cm1UFwM+/dT6CLLCXQqcLqkYA3wDGDWwVlyYgsNJXrpAoJXBhPgGcCvx37lweSWtP1y\npTzYLOa8vWv57hQKRUPBydqJE+knKDQV0tyhefVOioyEo0dlSMtVxCf38uzFzsSdiF695Pzzzz9Q\nWHjVmfrl+3ZDWRZtgGfV19M0jUJTIZ33JXH3kRJL2tq11b4vJ2snfFx8Kos5qNLVejlis6apa8uc\nA7APeB64YLqMpmmBQACQWMXu/wGjgDHALYAT8LOmaRY1PtrrhNI/wNDQ0j/y0j/Aq6x3djHWH10P\nwHDP4SzcsbDsS2A0QmCgbEL/9NONOk7ufMr3aTX4GLh/0DMAFP+yjlVt8yl0caJtrs0lr2PwMTCj\n8wyCwoN4r2ATXhOn8FvLV7i51c2YIv5g4bfnyibMmBjw9JRxhwqF4rrASe9ESq4M1K9gmbsQZqEV\nFgazZ1/VvNuzRU9S81KJ9feWnpXCQjmXX2Wmfmnf7vBAHvrnIYLCgypk45YnOSeZwLBAhsTpWLva\nlkhvKGjqAuHhl3Vffs3Pa+tl7s9aRRLE5YrNmqROxZwQ4hchxKtCiJWAqapjNE1rA3wAjAUKz9vn\nDDwKTBVCbBRC7ALGAd2BobU6+GsY8x9gdCtLCA4utfLcekK+5/jxGi9qa4w18lHURwBMajeJsMCw\nsi9BVBQ89pg8cPDgRh0ndz6d3TrTzL5Zab05q+aeAFgUm9DdeRdWrVpDYlVrmMpomkaRqYipxb+w\neNod+D/6GkXduvDd8gL2ffxG2YSpypIoFNcd5sLBAB4O1RBzUVEVhdYVzruhW0IRSCvYzsSdsn0Y\nwJdf1kifVoOPgQGtB/Bf7n/c3OrmKoWcEIJhy4ZxLvccbzneh8OP6+jt2IldjlkYvbms++rerDtH\nzh0ht7DE/nQRy5zBx8C7t77L0O+G8uiaR0tr71U1xpqmQcXMaZpmCSwD3hJCHKzikJ6AFfCbeYMQ\nIh44CPStk0Feg5hXO4P+m8X/XuxLm8enEPO1C/7PvSP/6EePrpmituUsf1GJURi8DYw85Uq7FT+U\njiEqMUq6FxMTZXJA164lg2yccXLnUyFubt48Vq1/H4BiDV6z3ESGVgA7d8qDyz2vUoxGCua8xeRf\nJ/PCnhfILMjE2sKaD7J/R+Tl4Xg4lhMu0C342bJzVFkSheK6o4KYq45lLiSkstC6gnk3wDOA6cbp\nWGgW7Dy1k30RywFINtxUI6WljLFGNsZsBGBjzMZKbk2AD7d/yLaT23gm4Blu/nAVGAy4nEiide/b\n5G/MZdyXX3M/TMLEvjP75Jx87JjcYRZz5Q0boaEcDv8MkzCxOHoxk3pNwhBHrXm3ymNZ659wecwC\nzgkhFl5gf3OgGDi/M3lSyb5KaJo2EZgI4OHhQWRkZM2MtISsrKwav2Z9kJCTQEZ+Bi+whoHtW+K/\n9xiH+/hxqqRKtsurr9L57jsxGjoxeEscB2bMIE3TZIxFNXGxssJ3xAgOvv46vf17Evv3Yt7+PoPD\nr7YhPjISDY3e9CbSaKTPr7+S3q0bBzZtqqU7rh+W/beM4oJiTmac5K3sDTz14wkAor30vK27H/3B\nheTY2fNPZGTp8zowYwZp/v64REfTceYbPDTahvCCZPSanlldZrE/Yz9+X36PBhx1s6BXYjEJQWOJ\nevFFdAUF3JKQQJxOx4lr4O+0prlWvr/1hXp+V05tP7ukhKTS/x/bc4yzVjXfA7oqNDRebf8qU/ZO\n4XjY/9FiiXRHpnXuyclh91eY0y6X6NRoZh2cxY3ON3Lz6r+IblXMiKUjmOE7A39XOUfm7fmLKW3X\n0rdpX0bajiQyMhLLrCz6nzlDrksrehf2vqznnpsrLXJhf4ZhZeVJl5dewgr4b8UKUk6coN2MV/l1\nysN0eP99tN9X8eKW7WwLgl0dnIhesoCc8AUcmfkmabX9PRFC1MsLyAImlHs/EEgA3MttiwOmlHs/\nFigCtPOuZQQ+u9Rn9uzZU9Q0RqOxxq9ZH9z53Z2CmYjBD2kixxIhQORYIf75bp7IKcgR235dJIRM\nxhZi+vRqX3fe5nkiIiaibENEhCi2tBAJnVuJcw4WYvr0/pWf4cGD8nO++KJmbq4BERETIVznugpm\nIixnW4oR422ECcSf7fVCuLmJU/cNEcUWOiGKi8W8zfPEni/fEsLaWhQFBYpMZ1theAjhMtdFPL7m\ncbFg1QIhhBCL548T+TpEkrOV0N5AnPBvJwSIzROGCHHokHyW331Xz3feMLlWvr/1hXp+V05tP7uv\no78unWeKTcW1+llV4fM/HzF1KOKr0LFyDnrrLbkjIkKIefOu6Jrm35NRK0aJQQ8hztghlr//qJj/\n+5vyt8WtqXjgGU/Rcn5LkZydXHbi9u1yDKtXX/ZnFpuKhcM7DuKZdc/IDT//LK9laSmEk5PYtWS+\nuG+ik8hv4iweeMZT3D0Gka9D7GxpIQod7ETQo07yN3DePHnv5anGswB2iGpoqobkZjUALYBTmqYV\naZpWBLQB5mmadrLkmNOABeB23rnNkNY5xRWwfN9y1h9fz4SUNiwPFyS3awFAVusWeD/xMsFPu7N7\n9tPyYL0ePv202qbySgGh3pBPMZ4HT/JFgI6Mvj0rnxQRIf8dMuRqb63BYfAxsDJoJRoy3m1jJ0s2\n3NGeW44VIJ58kn/bOqArNsHZswR4BrDgz3mQn49F+EpOWuZy0t2a70Z8xxfDvsDfVa5sB/2nQyfg\nq66FCB1ELgwhyUGjz7cR8O238oPbtq3VZBaFQtGwMLtZm9k3Q6fV7U+9MdZIQmYC397uwVR+w2Rp\nIQuXw1WFzJg7TSRkJnDWrwsPjrZiZMjXvBi0ABEUxJxnerDM/TRLRy2lqV3TshMPH5b/dux42Z+p\n03SyE8SZkiSIu+6CVq1keZLMTPxXbSVspcaIEQUscUukwE6P3gQ3JhRjmZXDsiX5eI17GpKSZGJf\nDfbgrjDOGrlKzfApMpGhR7lXIvA+YP5V34lMirjVfFJJWZLOwNa6HOy1xEf/fIRO0zG/yWieeqgp\nNufSAHA+eZYxo+CendlM2J6HsLGR/VFHj6YgcATLP3riktc2x8KNDBuJ32d+fPr2cGyLQDg58ej2\nQvoeza9pJIT3AAAgAElEQVR80h9/QJs24ONT07faIBjcdnBp/aL39fcycNtpZg8A08JPaSdkcG1U\n1E/08uxFcKwdAJu8NTqeg4MfmrhnzSGZHVaCj78BSwE/dJYZbC/9+RpJ782SE/g778iDTp2q0YlD\noVA0bMxirlrxcjWIOYOzv1d/nKydCAsMI9PSRPypwzX2GQkZCbSybYXF4KEk2wGpqWTa6HjD9Acz\nBs5gQJsBFU84fFi28brC2GE/Dz/2nN4jPYGRkZCXByNHyp0//IB+3AT2+Mos11dz5Rz7+Y1Q6GCH\nxR130j4hFxYskBmwt94KgwbVSA/u8tR1nTkHTdN6aJrWo+SzW5e8by2EOCOE2Ff+hRRup4UQhwGE\nEOnAIuBdTdOGaprmD3wH7AV+r8t7uVZIykpi16ldPOT3EE1mzMVryEjck3MxdfFFn19Eqi1ke3mQ\nbQWxzW3A15ecn39k9ChBQALVsvYYfAx0a9aNJtv28vWKEvGWk8P9o+C+GUtwiS5X/La4WF5zyBAo\nide71jCvWj+3H83wGctYPWsMMwbD/o/eoO3XqwEIXTmZ+59uxuC/kzjrqGPQBMHK10ZggQZTp4KX\nF54//CAv+MMP4OVF/yZ+PBGRwaRek+j+xHT45hv5DK2s4Mkna3TiUCgUDZfQLaEcOXcEKMtkrat6\nZ1GJUYQFhuHt4k1OYQ4GHwM2Tk1IORdfI9c3CROnsk6RlJ/EnfE2NMsopsjWGqeEs6z9xQW9hb7y\nSUeOSOOAvop91aC7R3fS89NJ+nl5mQh75hmwk4vtoi8/54a9CTye3p6AlbIt47Q7Lfhm5nBZgHnR\nIti/X7ZLc3eHTZukla4G5+O6tsz1AqJLXrbIhIdoYPZlXOMFYBWwAtiCjL0bJoQortmhXh+8v+19\nCooLmNZ/GqFbQrkxwx6AVTc7AzAutwNOgQ/QJA9WeqaRE3MEuxOJzOwdQrvbR1fL2mOMNRKVGEVA\nArxWYmPVioo42AyOfzYHx0OHyg7es0dmCQ0eXCv3W9+YV61hgWFMNPkT/8V7PJ0ri/vu8W0Cn30G\nQIsMQdcTeZxzs2WPpwXTB0znKee/iP5qDowfDyYTN3z8Mdx5J2zYwJku3rz6yV463zmurGbfAw/A\nE09IK14NlARQKBSNgwDPAF6LeA2Qlrm6rHdmdoXaWdmRU5gDgLWTK36OHWrk+meyz1BkKuLe/xwZ\n89ZqijX48MZCfu1owV070gheeajySYcPX5GL1Yyfh2zrlfLnBinkQP72ffwxAFtbFLEiHCYedSLb\ncAuZ1hpNm3kT1uysPH7HDvD1lb+VJQkVLFtWo7VT67rOXKQQ4v/Zu+/wKKvsgePfO8mkTHogQBII\nCUUFRaREUYqMBVwVEYWAIra1LLqWRY2KIqK7KsW6Kmv5WVZdIbBiwbrCAIItCAgIKCWhBBJISO/l\n/v64M2kklCQkmeR8nmeeMO/7zsw7lylnzr33XFXH5cZ6jo/WWs+rta1Ia32X1rqD1tqmtR6jTXkS\ncYIyCzN5NfFVJvSdwCkdTiE2IpY137wJwOM+P3LAHyI272Z4sikJ+E6slSsmlFGhFGc+Pt+kmd9/\nv+4gwVlWw/UhMjB8IImRcFVO1WIdXXMg+E/j2HvNNVW3W7bM/G2jwZzrV6s9xowbGXDtND68+kMA\nkrKS4Kqr0EoReriIFVcPJDSjkOAB5/GE/QkSxicwKuVpHI/fCLt2kTZyJHz1FZSU4L1iNXtfn8dN\n0/5dWbNv/X+eg8WLG13NXQjhXuwxdl6//HUA/sj4o1nrnbnYrDYKy5yBi81WNWaukfblmCH0Iw76\nsv/5J/CqgH1+FUy5zkZezyh6zP8QtlarbFZRYVa1OOWUBj3enDVzyCzKRKH47+U9wW5n59cLWPDY\nVSa7BgSOmUDcBCiPiaaTfycs3aII9Q1l7f616JEjzRhB1xi55005Ku6+u0mL4bemMXPiBB116ZB6\napRV7xJ9+eeXyS3JZfrw6YD5AHgwdCylFvg9VPNTNwuXHw7j1N9SKe7cgZ0dYFlP+Ka3BVJSICuL\nijGXk3pqV9ON9+abJrNWVmZ+gcTFkbZ0AQnjE+i/5TAJiyDTr+olF51rOXI8x/Ll0KcPhIc3bWO1\nEq5frdWN7jWacP9wkrKScOxbzUE/iMiFiz1OwbcM3i/7BUeSo2YtPn9/ts6cCbfdBkDmLZMZcO00\nwPw/fhP5MKfe+ViTVHMXQrifMaeOwdfTlx/2/WDqnTVjIAcmmCsqK6JCV4CfHxQUNMn9puSkALAj\n7irOHGrGrR0IgKlD78V/xRoIDoZx4yAnx9xg3z6TDWtgZi42IpYbPr6B8IBwNh7ciCPJwZAOH9H5\n8kng7w/BwXTOLGZFDBy+6xbYtw+/6N7cMvAWMosy2ZXpXE7RVZT5ssvM9c6dm7QYvgRzrdix1nlz\nzRTd/uCtNbJgsRGx4OlpFk2uZ+ZMXkkeL/z0ApefcnnlYHyA6AMFHAoPoswDKs6JxbY7hdIvlrI0\nPI+nLnyai5I9GLSvnH8N9abY5s2S0y14hXWBhQvNYvFnnQWBgfDoozB8OJMe+RD769/w5L/+IG4C\nJOmqJVDOKA7Gw1JtFbaSEli1qk3OYj2WmJAYkrOSSdyfSHGnUCJy4eHQsQBMmfiUCeAwgVr8UDMT\nLHj9evjoI5gxg+gFX9UI1AbsK8P20WeNruYuhHBP3+/9Hj8vPx4d/mjN5RKbic1qxpMVlhY2aWYu\nJdcEcx29OrJ+vVkSctR5zuElpdvN59yOHZXDURozkxWqJvGlF6Tz1favjlxCLCoKvdvUC40MjDSJ\njq5dK7u01+5fa45zFWXu0MGMZT54sEmL4Usw14q5grXlu5aTXpDOqz+/ytgFY9l6aCvXL7meRx2P\nUlZRxm1pb5Jz5Z948ZkrGd/7SoJ/WE/Bk4+x/Z7rIS6O1CsvpmDcGNa/+DBzvMyX+WtrX+Nw4WEe\nGf5IjcfM/3Ut60IK+SrlAn7J2AyANSePM8bfwX0/Kr5Y6EHcBJgzIYJx1yjG7PEl9Mm5kJFhBpl+\n8IHJ0lks8PXXkJsLzzzDv2Lhux4eWA9loIODKfZUnFJYa63Qn382v97aaBfr0cQEx5CUmUT80HgO\nBXsRXeiF/y4zemDQhddVBnCVHA76zppVf+atiaq5CyHcT/WxuU9e8GTN5RKbia/VfL4XlBY0eWbO\nQ3mQXJDMa0sfB+CmS6dXPceoCnj2WfjkE5NgcAVzp5zS4PJM9hg7k86YRF5pHt6e3jVmy6Z3sFGY\ntB2ASFsXUzkgMpIzOp2Bt4d35Q/xSh4eJqA71LRFnCWYa8XsMXbeGfsOo94fRdjcMO788k5yS3J5\n59d3cCQ78PLw4uo+V7MiBsaOK2bROwW8dONCut12P58/eT3nBS9i56md6PLJt6T2jWJUytPERsRS\nVFbEvB/mcUHMBQzpOqTy8Vb8/g3eyXs5Y8QERl/zKDNXe1DunFB6atf+MGMG1r8/TemIoSRlJdF1\n7BS8Fi8x2R6LBXr3NuvwPfccfPedeTMpRdnAs7jt5wr+knsqnXOhKCyEA8EeROfWWoBk2TJzPyNH\nNlsbtxbRwdHsy9lHWUUZO3zyicxVsGULdOlStRZgdYmJbJk5UzJvQogj1BibCzWHaDQTV2auoLSg\nyTNz4QHh/JH7B/fHTDYbw8NrPse77zY9PG+9BS+9ZLpDt21rcHkmR5KDL7Z/wTkR57A/dz8XvHtB\n5fbPCjcQdrgIH08fQnNKTUWGrl2xelg5q8tZdbd5p04mM9eEJJhr5VYkr6DcOVF34ukT+e2O3yiY\nXsDev+3FcYODyf0m46E8CAnrhmdJGdbsXIrGXcGtef9h9B4vIn7cAkDMD1tZ6XEr9hg7b69/m9M2\npfLalp41Hmtn4td4VkD0uZeA3Y7Xfz9GWTwo8bHC/ffD0qU4xg1gW/o2vDy8ePfXd82ixXVlexwO\nuOYa6NWLouAA4ibA7DeSOSUDsoK9SfYvJzyr1gTkZctg4MC6g5c2LiY4hnJdzpZDW9hqzSE4pwQ2\nbjQzoOoSH3/kcjiSeRNCUPfY3OpDNJpDZTdrWWHTZuZyU+ga2JVroq6hV7Ef+PqaoT1Ue45Kwaef\nQs+eZvJDQABMnNig8kzVs5w/3vojw7oNY9WeVZz/zvnELY7j/KGTCcwtoad3OCrFdAETGQnA4IjB\nrDuwjvKKWt91YWGSmWvrqo+T++3gbzz343N4Wby4MOZCliUtIy0vrTJ9vfOh23nxmSu5qLud2e/u\nr7yP8IWf8+cV2Tz/1n7S/RVbnetl9H1gNmVff8Wqd2fx0X896TlqUo3H/rPveeYfffqYv3Y7lmuu\nwavIlLZwREPc4jgWTVjE/efeT0l5CVcnXF136t412PPMM7EkJ7MiBra+/DgRuZDkXcjeQE2HjGpv\n7vx8+PHHdtnFCmbMHMDH2z5mfwAorWHDhvqDOSGEaMVOWmYuJ4XIABMskZpqJsvVVZPUZoNvvgEf\nH9P12cDyTLWznI4bHXhZvFi1exVTB0+lR//zAehf1sGMlwOzQgRmqFReSR6/Z9QqmCyZubav+ji5\nyR9NpkJX4OPpwyPDHzli3ENiJCQsVrz0zkF6HyqnaPw4tII/ovyYtRw+HR1Dt2zNt+d14cnhQEUF\nJZMnMf/NNH5/cQbKFTi5Zr66pnOfdpq5fvvtpvSFs7SFa2aqPcbOvUPuxWa1MThicN1pZNeYrZgY\nvPeloirAb/Tl+FZY2EgaewPBLz3bpKTBFFYsLW2Xkx/AdLMCfLT1I/YHODdWVFQF1kII4UaOCOaa\nMDNXGcwdOGCGotRn927z2PHxDS7PVDvL+d3u79BougZ2Zf7a+az3TAfg9MJAM3MWKjNzsZG1JkG4\nhIVJMNfWufr9r1x4Jb+m/YqPpw8fT/oYe4z9iHEPk+56Da8Hp9N7xUZS/cCybBk5Z54GOTlccS34\n7DW/Er7omMnTo3xIjARbRjbBxTD4rqfZMbQv/POfZop0XJxZpqR7dzMR4corzQzVagPsJz3xEfZk\nc55hfmH8ZdBfWJ60nKv7XF3/E4qJwaOklM750Bk//IorSPIpYl8gWMrKzXp1YEqSWK0wdOjJa9xW\nrFtgNyzKwq9pv1LcKbRqh2TmhBBuyNez1gSIkhJTtuo4ymbVJ7c4l5ziHDNrFEwwV18ZK1cFh8WL\nzcoLTVCeydXlekmvS8gryWPh1Qu59dcnAeiV72Uyc1YrdDTdYad2OBU/qx+JKbUSHp06maW9ysoa\nfC61STDXCtlj7HQL6gbAfefeV+NXQeWYgE2b4JJL4MEHITiILvnw05gB/HpOd/oc0hzqFcHQIXFg\nsXDptTMZvquM6EyYfR4U2bz4JrqciJQcM1D0xhtNmnr5clPSJC4OJk2CJUuOOsD+vvPuw8Piwew1\ns+t/Ms71VXtnexCSXQJAqj/sDXLu3+us97xsGZx7rnnTt0NWDyvdArvxwGo4X0dV7ejTp8EzsIQQ\noqUckZkDk51z1iBtyILzrrIkNTJz9QVzrqE+TThJzNXlenGPi8kqyqJPWB8eu/Y1ALplYzJzkZFm\nIh/gYfFgUMSgI3uvwsLM3/T0Bp9LbRLMtUKOJAd/ZPzBM+tC2bzwnzXHpC1eDGefbeq5/fwzTJ2K\n8rTyyiUd6L/kB4b0Nl2n9+pziN56APr35y7LEJZ+7Mc1cRbmju3ApOt8uCjVhu3t92DnTrOE1IgR\nZmzBzp1mbMFrrx2ztEVEQAR/HvBn3tnwTmVV7iM4g7mzCoP44NvnABPMZXQwv9o2//IVn2x9B9at\na7fj5VzjJGNCYkiMhL+9+zsVFkVhoM2s59fAGVhCCNFSatSZc/1Iz8+vCqquugruu++EFpx3FQyO\nDIzEUlwM2dn1B3MnoTyTq8u1b5jpMdmavpXunU4h1Q+6ZJaazJyzi9VlcPhgNqRuoLS8tGpjp07m\nbxNOgpBgrpVxpXG9PbzxHjKchMWKF5+5kpW/fQE33QQTJpjA5+674e23YdEiSEjg+1tHc+t1gfDU\nPyhTMHKPBX76yWS7EhPx+u8Szpx0DxmFGZw56Z6qkiI9epixcXfead5wJ7j0U/zQeDSauWvm1n1A\ndDQAffN86a/Nag+p/lQOEF3w1VzsyQq0brfj5VzjJL09vFkRAwmPjUdrjfLza/AMLCGEaEn1ZubA\nfJ4VF5syVicwMaF6Zs7rsLMAfQusFtQnzIxl3nJoCym5KewNgpD0PJOZ69q1xrGxkbEUlxez+eDm\nqo2uzFwTjpuTYK6VSdyfyBtj3iC/NB898ny8Fi9h8X/KGBI7Dt55B4YPN/Vynn/eFEN0ftEPCh/E\nos7pvPrASPYHQMzS1ZCXZ4K5+Hgc0fDexveYMWKGqZQdTdUvFFeauwFLP0UHR3Pdmdfx+rrXSctL\nO/IAHx8OBXnSM9tCP21ewAf9IM2njEJPuK3zpQzcnm3e7Gef3VTN6FZcYyG/2/MdAPeVLiX73IH4\nHDjU4BlYQgjRkmoEc9UzcwDffmuW2IqOPqHkQfXMnFdGhtl4tAkQJ0m4fzhB3kFsObSFfTn7zIS+\n1Iy6M3MRg4FakyAkM9f2xQ+NJ9zf/NLoGdoTzj4bz/xCvAtLzNi2VaugVy/nwVVp5IHhAwFIX/kl\nf/QMwpLqDKzOPZf1/3mOVX+9nITxCZULtteoBt7IsQUPD3uY4rJinv/x+Tr3J4UoumWUmWnkFgv9\nzriAXVlJFHQOJSpHEbJunenm9fI68QZrI+wxdm4deCsA8zwvI/SPPSecJRVCiNaixgoQ1TNzDocZ\nkw2mR+YEkgcpuSmE+IRgs9qqgrkWyMwppegb1tdk5nJS2BsMnjuTTYBaKzPXM6QnIT4hNcfNSWau\nfdiZuROAXqG94L33zAt+/HhYurTOF/ycNXPILc4FYE2XUoZtKwQgP8Qfdu/m1Dsf44rJT9ZfDbyR\nYwtO6XAKE8+YyCuJr3C48HCNfeUV5WwPLKXzoUJITaU4NIj1hzYyY8QMtvjkUvjDd/jt3t1uu1hd\nHEkOPtj0AW8FTOGKmR+w/sWHTzhLKoQQrYWPpw9QR2YuMdFUUQDYs8f0Hh0jeeAaV5ySm1I5k/Vg\nykazswWCOYC+YX3Zmr6VlNwUsjoGoFwzU2tl5pRSDI4YXDMzFxpqJklIZq5t23F4BwA9N+yBBx4w\nG//xj3q/2GMjYrn505uJCIhgRQwsfXCc2REcDBMnYvvoMwZcO63GbZq6Gvj0YdPJK8njpZ9eqrE9\nvSCdXcEQdCiHjO0b2W7NqcwQ9j5zJL57D5gD2+nkB6hZYfymsjPY+/o8RqU8bTKnskyXEMINWZQF\nX09fswJE9cxcfHxVySWtISnpmMkD17jirYe2EhkQiSPJwS/bP6fCw1KV5WpmfTr24WD+QX5N+5Wi\niGrnUCszB6arddPBTRSVFZkNFospXyKZubZtZ+ZOugZ2xXv9RvjTn8Db2yxLUs8XuyvTllFg0s5/\n8f2WnNN745e0r9nGXPXr3I+xp47lxZ9eJKc4p3J7al4qScFgKa/AZ8NmwnsPNBnCOXPo4mveAPl+\n3mZ2bjstwVGjwnh8PAOunVYzcyrLdAkh3JDNaqt7zFx2dtVBO3Yc835c33HbM7aTlpdG3OI4/uTR\nH0vnLpVlQJqba0brL/t/QVcP4Gpl5sAEo2UVZWxI3VC1sVMnycy1dTsO76BnSE/zBZ6TY37FeHiY\nnfV8sdtj7Fx35nUAzPW8lMC0zGYfc/XI8EfIKspifuL8ym2peakkB5t/+2Xl0yHauaJBbKxZOw8o\nGHg2rFzZbktwtIZ1FIUQoqlVBnO1Z7OeYDAHMDRqKBVUsCFtA1MHTyU8T7XI5AcXVzB3/2pN7wKf\nqh3h4UckJuqdBCGZubZt5+GdZrwcwObNcMYZx7yNI8nBJ79/wlsBUxjz2PstMuYqNjKW0T1H8+wP\nz5o3MM7MXEi1g1xvPrsdHn4YwNQLOoFaQ0IIIVo/X6vvsTNz27cf130t/m0xAGNOGcP8tfMpO7iv\nxcbLAXQL6mZWd4iEa+avNhnCzp1hzZojEhNdA7vS2a/zkZMgJDPXduUW55KWn2Yyc5mZZqrzMYK5\n1jTm6tERj3Ko4BBv/PIGAGn5Zh1W7cosVv8ldeed0L8/HZzFjyWQE0KItuOYmbmoqOPKzDmSHNzx\nxR0A3H3O3SSMT0Af2s9+f30yTvuY5qyZw8rklZzW8TRWxMDqeXej0eSVFtSZmFBKERsZK5m59mRX\n5i7AOZP1t9/MxmMEc61pzNWwqGGM6D6Cud/PpbismNS8VHx9/VHdzPJkNYK5desgJYXkKVOkBIcQ\nQrQxNquNwrJC5qxzzl6tlZn7IzrguIK5xP2J3BFrgrmooCjsUSPoVADJvsUn5byPxTUhI9TXrKO9\n6fSObO5iwf9wbr2JicHhg9l6aGtl5QnCwiAry6xZ2wQkmGtlKmeyhvY0XaxwzGCutY25enT4o6Tk\npvDur+8y+P3ljN0fWLmsF507m6Dt9tsrf8Ek33yzlOAQQog2xpWZi408m3wr7DmwDYDduzdR7AFe\n/QfC7t3HDGjih8bj7+XPA6uh+7pdcOgQlgrNebFXtcjEOdeEjO/3fg/Ayrcf59Qi/6OOU4+NjEWj\nWXdgndngKhzcROuzSjDXyrhqzPUMcQZzAQHgymq5iYt6XMTZkWfz9Oqn+TG8nJffOWjWfQXzxo2L\nM/9u4kWQhRBCtB6uYM4eY8caGMw3Gz+mzyt9cPz6MSo4mOjYi6GiwpQnOYY92XvY3iMI72unVE6e\nIz29xSbO2WPs3DLwFkYmwYcJFWaJzKOMUz9iEkQTFw6WYK6V2XF4B2G2MIJ8gqomPyjV0qd1QpRS\n9OnYh+SsZP7pt5nn/3YurFyJBgru/at5sb/2WpMvgiyEEKL18PX0rZwM5+UfRBdLANvStzHQ1gOv\nkI5VqxkdR1frnuw97BnU03x/uOqvPv98i02ccxV6f9j7QiZP9DRLZEK9iYlOfp2ICoqqGv7UxEt6\nSTDXyuw4vMN0sWp93DNZW6Prz7weD2UmPWQMOZM9ky9HAQdvuFomOgghRDtQOQECyPeCkpwsANL2\nbyfHx1IVzB3HjNY92XuICooy3x+DTZaLW29tsUDONelw1Gvfcs9DH9dcIrOexESNlSAkM9c2uZYr\n2ZnpLEuSlgYZGXxrq2PxejdwQY8LmD58OgCeK7/D9p9FJN81hegPv5RxcUII0Q7YrDYKSwtxJDn4\nozCFzhZ/APr7RLOhKAlH7iYICjquzNzenL1EBUaZ7481ayjz84O3326R75Makw6pY4nMesRGxLIz\nc6dZ9lIyc22Ta3bMxM/3cGGyhQ3/ex+ADrEj3HZlhJnnz+Tu/H488vJGPnviOqJf+rdMdBBCiHbC\nlZlL3J9IdOTp+JaYUiL+heX06XEOiQfWmuzcMYK57KJscopzGLqjxHx/RESQ3a9fi32fNHTSYWyE\nGduX/viDsH49eHpWZeYa+T0vwVwrYY+xM+/ieSRGwriZC/hl/gwABvjGuO3KCKt2ryL0t50sfXIK\n8eVfylqjQgjRjrhKk9x/3v2EhEZgLTKzVlVODmERvUzw06vXMbtZ92TvAeC05Fz44APYu5e8Hj3c\n7vtkUMQgABIjNEycCIGBJjPncDT6e16CuVYkzC+MFTFw5VUlXP9LGfj6mhIebrgygmtMwYiXl3LT\ntH+TMD6hakyBTHQQQog2z9fTF8AsMO/nh1dRGQCeuXmmexWgd29ITj5qeRJXMFfwt7+aVR/Kysjv\n0cPsdJPvkzlr5rD+wHp6h/ZmcZcMSEhAZ2eR8fXHTbICkgRzrcjXO74G4KxJ95LiXwGFhW67MkJD\nxxQIIYRoG2xWs/JDQWkB2mbDu6QcSwVY84uqgrlevUx5kt27670fVzAXFRQFmzYBVAVzbsI1lKpb\nYDfW7l+LIxp2B0GHvelN8j0vwVwr4Uhy8Ma6N7BarDznNYbuWfB7Jwslr7zkluPLWlshYyGEEM3L\nFcwVlhZS5uOFXwkEuBZtqB7MwVG7Wvfm7MVqsdLFvwts3AhWKwVuVn/VldD4KeUn9uXs44VnxhKV\n5wEhIU2yApIEc61E4v5EBoUP4pr0LqiJE1GenvhecTUfPT5RJgwIIYRwO9Uzc4VeFmylEFRfMHeU\nSRB7svfQNbArFmUxmbnTTkN7ep7EMz857DF2Lu19KSOT4P0FpVguHwPl5U0ykUOCuVYifmg8eaV5\nnH/QZgrqlpQQ1W8Yk+56za0GeAohhBAAvlYzZs4Ecwq/Uggucu50BXOdOpmVjo4RzEUFRZkrmzbB\nmWeexLM+eRxJDr7Z+Q2xKXBtnAe7YoIhJwfOO6/R3/MSzLUiSZlJ/HLdhVXLd3Xvbv66yQBPIYQQ\nwqV6Zi7farZ1yXPudAVzSh2zPMme7D10C+oGmZmwdy/063cSz/rkcE0KnDdqHnOHwaDJ9/PP5ASz\n89ChRn/PSzDXSmQWZpJdnE1MSAzsMYM9K4M5IYQQws1UD+byrBUA9Coy2yqDOThqeZLyinL25ewz\nBYM3bzYb3TCYc00KHN1zNAARARHcfLEzeGuCVSAkmGslkrLMQsM9QnpUzeqRYE4IIYSbqpwAUVZI\nrkc5AH3LQszO6sGcqzxJaekR95Gal8q078oZsr2wciYr/foRvH69WxXTd00KDPUNBUwCp1+/C81O\nCebajqRME8zFBMeYYM7fH4KDW/ishBBCiIapnpnL9jQ15noWmXF0R2TmysqqeqWoWuJyT/YeEiNh\n9PT/49BH71Pk7wPbt9N31iy3LKZvs9qwWqxkFmU26ZJeEsy1Eq7MXEyIM5jr3t2MJRBCCCHckKto\ncEFpAZnKTGONzDNhhw4MrDqwjvIkrrpsX27/khUx8O0ztxPs+MGsmjBxIltmznTLGqxKKUJ9Q836\nrGFhZqNk5tqOpMwkgn2CCfYJNr9OpItVCCGEG6uemTtsMcFcaFYxJRbIs5RVHdi7t/lbbRKEqy7b\nc4KAqycAACAASURBVD8+B8D1WW9h8fLCZ/9BmDqVrAEDmudJnAQhviEmMxcYCF5ekplrS5KykkwX\nK1Rl5oQQQgg3VSOYU6YmSWBGHtk+cLgos+rAzp3Bz++IGa32GDtndDoDgGcZjUdRCZx/Psyfb8bM\nuakQnxAyCzNN71tYWJNk5tyv6l4blZSVRN+wvpCXB4cPQ1RUS5+SEEII0WCuOnOFpYUcUgUA2NKz\nSfOH3MLDdA92Ji1c5UlqzWh1JDnYkLqBy/f5ccUHH5qNU6ZAjx70HTcOzjrLLbtaQ31D2Z+731zp\n1Ekyc21Fha4gKTOpavIDSGZOCCGEW7MoCz6ePhSUFnBQmwJzHqVlZPtguhmr6927RmbOVZftrC5n\ncfHhYA4++FcAflUHwW43Y+bctJh+ZTcrNFlmToK5ViA1L5Xi8mITzEmNOSGEEG2Er6cvBaUFpOm8\nym3Z3pgJANX16gVJSWZWK1V12TSaL8adwalnnA/AeksagBkz56bF9EN9QquefxNl5qSbtRWoLEsS\nEgPrJDMnhBCibbBZbRSUFpCqcyq3ZfvUE8yVlpqERo8exA81gdqh/EP0Du0N+w8AcOOl05vt3E+W\nEN8QcopzKK8ox0Myc21HZVkSVzerpyd06dLCZyWEEEI0js1qo6CsgP1lWZXb6szM1TGjFSC9IJ0w\nWxjs32++Gzt2PNmnfNKF+JjCyVlFWSYzl58PBQWNuk8J5lqQqyiiKzMXHRxN2m8/kxUWCB4eLXx2\nQgghROPYrDayirLI00WUWc33Wp6vR92ZOagRzBWXFZNbkktHW0cTzHXpAhb3D1tcq0DUqDXXyK5W\n92+VhnA4WmwZEFcAB1VFET/74zMCvAL4cd+PJG9ahe4uM1mFEEK4P5vVVjlzs8zXG4BSf19TmqO6\n8HCw2WrMaM0ozAAwwdyBAxAR0TwnfZKF+JrMXFOuAtG+grncXBPIxcWZZUBaIKhzBXCOJAfDooYx\noe8EEvcncv/qCl585kr6F4cQcsqZ5uAWDDqFEEKIxvK1+pKSkwJAhY8PAGUBfhwuqpWZc5UnqZaZ\nSy9IB6jKzIWHN89Jn2TV12dtqlUg2tcEiO3bYexY+OQTcz0uDhISmvUUXFWtr1x4JcVlxRSXF6NQ\nOMLyWbrQF5+cXDP5wRV0NvP5CSGEEE3FZrVVZti0n6k7VxEUcGQ3K5hgbsuWyquH8k22qjIzN3z4\nyT/hZuAaM3e48DB0co4VlMzcCdDaZOfGjYMJE0yg1AIFB/28/Mgvyae4vJjzu59PqG8ow2+YwbRL\nPcw5rl9fFci5YUFEIYQQAqpWgTBXnP8OCq4/mNu1C8rLgarMXJhHIGRktM1u1royc3PmmITOCWhf\nwVxoqJkNk53N5sHdWyRQ2pu9l0vevwSNZnyf8azavYrpw6fzhP0J4iMnmYOWLoWpUyWQE0II4daq\nB3PKLwAAj+CQuoO53r2hpAT27gWqgrlOuSa4ayvdrDUyc/7+4ONTM5iLjTUJnf/7v+O+z/YVzHXs\nSJm3FYA+K3874ci3sfJK8rC/ayerKIs3x7xJbGQs80bN4+nVT7P65//S8+X3qbB68v2NF8L8+c1+\nfkIIIURTsnlWBXOe/iaYs4Z0rD8zB5Xj5lzBXHBmodneRjJz3p7e2Ky2muuzVu9mtdtNIHfrrXSD\n43rS7SqYK9+xnRvHm2GCHmcNMJGvM2CqPsvUxZHkYM6appmAUKErmLJkCrsyd/HUhU9x04CbiF8D\n04oGkDA+gS5/exSKirDcdz/n9RllulirnZ8QQgjhblzrswJ4BgQB4B3aiYLSAorLimseXEcwF+IT\ngmeqM2vVRoI5MJMgKpf06tTpyAkQX30FWtMJjisd2a6CuR0hmh7X3wPe3mYgZUICKxbNxZHkqDHL\n1JHk4PbPbiducRyxEbFN8tiPLHuEj7d9zPOjn+ehYQ+Zjc5Uqn35Lnr98Ls5pzffNNvtdhPQuena\nc0IIIYTNauOB1XDJXm8sfv4A+HbswsgkKHn67zUPjogAX9/K8iTphelVM1mhzXSzgulqrcxO1s7M\n/fKL6Z3z8eEgHDie+2tXs1lLbd7M/+VfPNwxGN8DB8BuR0dD3OI4fsy4iq/7PcSw/1xGaUUpflY/\nHFGPMWBRIsQ3buzauxve5Zk1z3D7oNu5+5y7q3bY7fDhh3DppSbA3LIFFi2qGitnt8u4OSGEEG7L\nZrXxXSQsXlAKI7MB6J6cRcIiyHi/BwHVD7ZYoGfPyszcofxDJpjbeaDNrP7gEuIbUjMz55rFW1EB\n111nul8TEth7xRX7j+f+2lVmzqIsJIxPYLNHBod3mYZzlQq5N+MDut/+AOdsL6SsooxpRQMYcM/T\nJkt2gqp32a7es5pbP7uVAV0G0D2oO2ru3Jpdp998Y9ajKyqCO+6Q4E0IIUSbYbPaWBEDD9wSBcuX\ng8XCeY/8i7gJsG9Q7yNvUK3WXHpBOmF+YVU15trA6g8uob6hVYWTO3UymTmtzVi5bdvgoYdgzJjj\nvr+20zLHobSiFHuMnejThlB+IKVye5+wPnzetYDx4zWLE2DpQk/unLOCdS881KDgytVl+8HGDxi3\ncByd/TuzJ3sPQ7oOqZql4nDAwoUwd6650ZQpMulBCCFEm+Kazbq9f1e44QaoqCD9hvGsiKljfVYw\nM1p37jTHFaTT0bdtFQx2cXWzzlkzh50eOVBYCHv2wEMPkXX2mcy5NOiE7q9dBXNl5WWUlpcS1utM\nwrJLK7cv2LQAjeaHXt7s6Gjhsq1ldCgEzwfiSb77eli7FmbPPjLQcjjo9uGHRzyOPcbOv6/8Nzd8\nfAN5xXnkl+SzaMIi7DH2qrFwV18N115rbjB7Nvz73zLpQQghRJvi62kmQIzYWQ6LF/P9jRcS+m4C\nI5Oqgrkakw179YLiYvTevSaYa2NLebm4JkBc/dlOPv/p32bjLbdQkZ3FY1E7uXrprhO6v3YVzAGk\n5qWaxXoPH4biYhxJDmasmAHAZ12mEXvAQrpNke+l8MKDqFfeN9m0uXPN2LannzZdos4VGnJPO63O\nx/GweFCuyykqL+KvZ//VBHIudrsZF1BRYYK6+Piq7TLpQQghRBths9oYmQQP/nMdJCRQ/NgjTJqg\nSFgEfqt/xpHkqDnZ0DmjtWjbZorLi6smQLSxYC7EJ4SC0gK6XXQVU9c6Q7Fvv+XT0z2Zt9yTnqMn\nndD9tbtgbn9utXRtWhqJ+xMZd9o47Elw0cNvYLHZKLnsEp596Hw6ZJfy8wvx8O67Vd2t06dD376V\nKzRkDRhQ5+Ms37UcgL+e/Vfmr51fs+zJsmWwbh2ceiqsXFkzE2e3VwV3QgghhBuzWW3EpsC7D18K\ndjv2GDt3PfgRcRMgfeWXxC2OI2F8QlXCo7cZR5e/ZQMAnT2CTPKlrXWzOleBODykP5v+Znrpcrzg\n4mQPvBYvOeEhXu07mDtwgPih8Wg0F2UEo+bPh5wcIoZdwvSZ/2PazZH84HiXiinXmVmmWVlw9tmQ\nlGT6/utpbEeSg5cTXwZg9kWzSRifUFn2BIcDxo83Wbnp06VrVQghRJs0Z80ctqVvY+4wyBhyJmC+\nH3858Asb+4ZyxxnJ3Drw1po9V5GR4O1N6e/bzNUCD7O9jWXmQn1DAcgszOTZqBRyvCCwBF4drHFE\nn/j9tftgDiApM4lvru5vltUA6N8fT4sno255mmn9U1mydYnZ/v338Mcf5t+vvw63307w+vU1H8Dh\nQM2dy6ieowj0DsRmtVXOmE3cn2i6UC+5xEyzvvxy6VoVQgjRJsVGxPL4yscB063o6lL1tHhSXmGW\n6Hrhxxdq9lw5y5P4L1vFyCTonONcyisiwiQ95jRNIf+W5lrS69td35K25mvKvDx4xm7l1sRyXnzm\nyiMWMTiW9hXMKWcw16WLue4K5rKSiAmJgV9/NdvPNL8grul3Dad0OIVZK2dRsXyZyaAtWgQBAXD+\n+bBgAWfMmFGVVXOOoxs54QGsHla6+HepfGh7jJ34ofHwwAOmi3XkSLNWLEjXqhBCiDbHHmPnhdEv\nAOBINoHcw8Me5unVT7Nk4hJuGXALRWVFXJVwVc3gpVcvVEEBCYsg/IfNZtu+feY7uAHlwlojV2Yu\n4/PFfJhQwVf/uIm0+DsZf3U5CxZp0pYuOKH7a1fBnNViJSU3xdR0UQpSUyksLWR/7n5igp3BXFQU\nhJiI2dPiyWMjHmPTwU1s/vwdk0G76CITyP3+O3z8MWl2O0yYADffXDmODrudA7kHCPevo49/61aT\n3Rs3rnmfvBBCCNHMru9/PZf1voxPfv+EqYOnUlZRVjlGbt6oeUQGRhLoFcj3+743N5gzB7y98UnP\nYuLVEPrKW2b7PfdUfr+2Ba4xc913HCRuAvS86hbuHXIvK2IUbz90CZMKe57Q/bWvYM7DajJznp4m\noDtwgN3ZuwHoEdLDBHP9+9e4zaQzJnFax9OY3GsDFSPPB2BZD8xyI716sf2++6B7d3j7bX66qE/l\nCy01L5XwgDqCuY8+Mn/Hjj1pz1MIIYRoDVYkr+CnlJ+YMWIG89fOJzYitnKMXJBPEG+OeZM9OXvI\nK84zN4iNhS+/xLOkjE5FCsrKzParrmozgRxUdbP+pe8u1p7qz8DwgXQP7k7c6XE8WP4V2XfffkL3\n166COS8PLxPMgelqPXCApMwkAHr4RJhsm7OL1cXD4sFjIx5j88HNLN6yGICA0SYQ27rgZcKWLUOv\nWwfAwM/XVXa5Hsg7QBe/LhxhyRI45xwzyFMIIYRoo1xj5BLGJ/CE/YmakwGdRvcazZ8H/Jk538/h\n55SfTcA2cyYACxZpVHExXHGFWXi+DU0UDPYJBqCkvIThUcOxelgBeOC8B8gtyeX1X14/oftrV8Gc\n1WKtCubCwyE1laQsE8z1Ti2B8vIjMnMAcafH0adjH2atnEV5RTlnX3IzJR2CSX3tWXrPfooK5bz/\nG26CuDgKv/mCvJK8IzNzu3eb8XJXXXUyn6YQQgjR4hL3J9YoO1JjMmA1z456loiACG765CaKyopg\n8mTKXTvj4uCTT9pU5Yc5a+awavcqgrzNKg/2aHtl4eQB4QO4MOZCXvzpRUrKS477PttXMOdhJbMo\nk8LSQhPMHTjArsxd+Hj60GGHc3mvOoI5D4sHM8+fyZZDW1i0ZRHMm8ehmM6cvbscVVbBL5cPMpMi\nfv4ZEhLI/34FQI0JEAB8/LH5K+PlhBBCtHHxQ+Nrlh2h2mTAaoJ8gnhjzBtsObSFWStmwbZtlFgt\nfHNuJ7Oeq8PRpio/uJb8dK2O4e/lX6Nwcveg7qTkpvDhpiNXmKpPuwrmvCxegOkCJTwc0tJIPryL\n6OBoLBs3gc1mVmaoQ3JWMtFB0cxaOYvN3X0J3Pg7fmWgLfB6wO9UFBZAbi7Y7Wy9ySyOe8QEiCVL\n4PTTK4siCiGEEAIu6XUJN591Mz9/MJvS8VcxdWpX3vjbiJoZuTZS+cGVoUwvTMfLw4vHVjxWI4M5\nud9kPJRHZVmX49GswZxSaoRS6lOlVIpSSiulbqy1/0ml1DalVL5SKlMptUwpdV6tY1Y4b1v9clxz\neF190pXlScrKGPXe94zdH2gmP/TrBx4eddayOTvybDIKM9iWvo2Bf9zHXy4323/rpHj1Kw++PNVC\n2Z5kqKgwS4ZRKzN36BB8951k5YQQQog6PDf6OYamenP9JG8+jcilo29HsNtZ/+LDrFg0t6VPr0nZ\nY+zERsRSUl7C1MFTa2QwL+hxAQ+c9wDJWckQyHFVS27uzJw/sBm4ByisY//vwJ1AP2AYkAR8pZTq\nXOu4t4HwapfjmvZRI5hzFg7e5J3NIy9vhLVrTRers1Zc7Vo29hg7SyYuwUOZNVc/HeRP4shTGXBA\nU/jnG+h57V145hdCcrLJ/EHNMXOffmpWfZBgTgghhDhCkE8QtkdmsqBTGplFmXS0dcSR5GBUytPo\nBx5o6dNrUo4kB9sPb6+c5Vu7SPAs+ywCvALAj+Nax6xZgzmt9Rda6+la68VARR3739daL9Na79Ja\n/wZMAwKAs2odWqC1Tq12yT6ex7dajgzm/vAr4oe/XA55eVVFCeupZXNhjwu56+y7qNAVvOQzjrM2\npvHECPB5821OC4wxB23aRGpeKlaLtbIoIGC6WLt3h3rWchVCCCHau4eGPcSfev4JMBMojli7tQ04\nnlm+a/asMQmofA4cz316nrSzbSSllBdwG5ADbKi1e5JSahKQBnwJzNJa59ZzP7c574fOnTtjVVZ+\n/O1HfvQJYwgQngtdV22hwmrF8sUXJE+ZQrJSsGLFEfe1PnM9b299m6fyL2bMnPf5b/ytPKnegrMH\n8fAjj2AFkj79lPWx+wm2BrNq5SoAPAoKGPrNN6SMHcvOlSubpH3akry8PFbU0d7i6KTdGk/asHGk\n/RpO2q5+d3S5g60HtvL1zq+ZEjUFtVuxYveKGse4c/st2LOA6b2mVz4vhWJ6r+ks+G4BardifeZ6\nZm2dxcw+M5mWM23/cd2p1rpFLkAecGMd2y937qsAUoCza+2/DRiN6YqdhOmK/d/xPOagQYN0zAsx\nevJ/J2udn6816Pf6oTVobbNpPWOG1h07ar18ua5t+a7luuOcjnr5ruVaz56t133wrO44p6OOmh2l\nR7w9wtwmJETruDg9+r3ROvb12KobL1xoHmPlyiPuV2jtcDha+hTckrRb40kbNo60X8NJ29XP9X07\nY/mMqu/dWtpy+81ePbvyOQNr9XHEN60xM+fAdKt2BG4FEpRS52qtDwBoratX0tuklNoF/KSUGqi1\nXnesO48IiKDfu1+yvuhf9PXz4bpNRWhg1+1x7E1fy0jXzJlaXa016uXE2xkAJCQN4O6P72bt/rWU\nXT8cz+HDTTer3YuooKiqB12yBMLCYOjQJmgeIYQQom2q3gVpj7Fjj7a3ya7Wo6lduuV4tLrSJFrr\nfK31Dq31j1rrPwOlwC1HuclaoBw4rnofEQERrIv0oNtt95PjbbZlnjuAoNffJWjYRfXWsqmvXs4V\nEVdQUFrAlkNbzGzYP/4gI2t/VVmS4mL4/HNTwdrD47jaQAghhGiPjrfQsKipNWbmarMA3kfZ3w/w\ngGMPEswtziUiIIK3uhXx87P3cdEd8yixAL9uYO8b8xhw7TRzoN1+3GvA9Q3sC0DRU09AUF8oL6dj\n8iG6nNPFzIx97z1Tf05WfRBCCCGOqq6slD3G3m6ycg3V3HXm/JVSZymlznI+dpTzepRSKlAp9Xel\n1DnO64OUUm8BXYEE5+17KqUeU0oNVkpFK6UuBRYA64E1x3r8XZm7KCwtJLcklwkZr/JhP/CqgI1X\nD6sK5E7AnDVzSCtKI8QnhFVhhfDyywD0S4NBv+eY7tq0NLM6xIUXnvD9CyGEEEIcS3N3sw7GBF7r\nAV9glvPfTwBlwOnAEmA78BnQARihtd7ovH0JcCHwNaYm3UvAN8BFWutyjqFHSA8+3GyWxxjyRyGX\nbVesmDKcMz5azfr/PHfCTyY2IpYntj5Bz9CevNdxHxteeBgNTE2E0dPfgg8/NN21l14K3kdLLgoh\nhBBCNEyzdrNqrVcA6iiHHLWirtZ6L3B+Qx8/wDuAMYPG8Mt/nmXBIs3eN55l5LXTWH/Jc3S77X7W\nwwll6Owxdmb2mcmjWx+loLSAC32fYUd4COemZHLgptGEe3ublR+kULAQQgghTpJWNwHiZMotzuXd\nX9/lmsJe3HStjaxzTQHfAddOY+/r88he/e0J3+eAkAHceNaNAExOj8A3v5gyBV3e/S88/zx4eZnM\nXB1LhAkhhBBCNFa7CuZ2Ze4iYXwCty3czn3Tl9aouDzg2mmMfPWLE77P9ZnrSfgtgeszujHjlc3M\n+9sQZg8DVVFhSpIMGGCWCqtjiTAhhBBCiMZqV8Fcj5AeTTrd2ZHkYNbWWSSMT+DK3EjiJsBjysHs\nPwXAxRcDkJ6666hLhAkhhBBCNEa7CuYCvANqXLfH2BtUnM8lcX8iM/vMxB5jJ/ixp1jd0xONxtfq\ny6rn7sXR20rH3Ydg6lQJ5IQQQghxUrSrYK6pxQ+NZ0CIGXdnj7Hz6IhHAbBZbTz3z2sYmmGDGTNg\n/nwzZk4IIYQQoolJMNeEHh3+KJ1snYhen8wHC8vwWrwEnnjCdLHGxUlAJ4QQQogmJ8FcE1q1exUV\nVPCQ1wVMnuiJI9q5o54lwoQQQgghGssdlvNyC7UXB/aqdf1ElggTQgghhDhekplrIrI4sBBCCCFa\ngmTmmogsDiyEEEKIliCZOSGEEEIINybBnBBCCCGEG5NgTgghhBDCjUkwJ4QQQgjhxiSYE0IIIYRw\nYxLMCSGEEEK4MQnmhBBCCCHcmARzQgghhBBuTII5IYQQQgg3JsGcEEIIIYQbk2BOCCGEEMKNSTAn\nhBBCCOHGJJgTQgghhHBjEswJIYQQQrgxCeaEEEIIIdyYBHNCCCGEEG5MgjkhhBBCCDcmwZwQQggh\nhBuTYE4IIYQQwo1JMCeEEEII4cYkmBNCCCGEcGMSzAkhhBBCuDEJ5oQQQggh3JgEc0IIIYQQbkyC\nOSGEEEIINybBnBBCCCGEG5NgTgghhBDCjUkwJ4QQQgjhxiSYE0IIIYRwYxLMCSGEEEK4MQnmhBBC\nCCHcmARzQgghhBBuTII5IYQQQgg3JsGcEEIIIYQbk2BOCCGEEMKNSTAnhBBCCOHGJJgTQgghhHBj\nEswJIYQQQrgxCeaEEEIIIdyYBHNCCCGEEG5MgjkhhBBCCDcmwZwQQgghhBuTYE4IIYQQwo1JMCeE\nEEII4cYkmBNCCCGEcGMSzAkhhBBCuDEJ5oQQQggh3JgEc0IIIYQQbkyCOSGEEEIINybBnBBCCCGE\nG5NgTgghhBDCjUkwJ4QQQgjhxiSYE0IIIYRwYxLMCSGEEEK4MQnmhBBCCCHcmARzQgghhBBuTII5\nIYQQQgg31qzBnFJqhFLqU6VUilJKK6VurLX/SaXUNqVUvlIqUym1TCl1Xq1jvJVS/1RKpTuP+1Qp\n1bU5n4cQQgghRGvR3Jk5f2AzcA9QWMf+34E7gX7AMCAJ+Eop1bnaMS8AVwPXAMOBQGCpUsrjJJ63\nEEIIIUSr5NmcD6a1/gL4AkAp9U4d+9+vfl0pNQ34M3AW8LVSKsh5/Sat9f+cx0wBdgMXAV+fzPMX\nQgghhGhtWu2YOaWUF3AbkANscG4eBFiBb1zHaa33AluB82rfhxBCCCFEW9esmbnjoZS6HFgA2IAD\nwMVa6zTn7i5AOZBe62Zpzn113d9tmKAQIE8p9XsTn3LHOs5HnBhpw4aRdms8acPGkfZrOGm7xmkv\n7df9eA5qdcEc4MB0q3YEbgUSlFLnaq0PHOU2CtB17dBavw683uRn6XpgpdZqrQefrPtvD6QNG0ba\nrfGkDRtH2q/hpO0aR9qvplbXzaq1ztda79Ba/6i1/jNQCtzi3J0KeGACveo6YbJzQgghhBDtSqsL\n5upgAbyd//4FE9xd7NrpLEvSB/i++U9NCCGEEKJlNWs3q1LKH+jlvGoBopRSZwGHgSwgHvgMM1Yu\nDFOmpCuQAKC1zlZK/R8wVyl1EMgAngM2At8241Op7qR14bYj0oYNI+3WeNKGjSPt13DSdo0j7VeN\n0rrOoWYn58GUGokZE1fbu8AdwAfAOUAHTKCWCDyltf6p2n34AHOBawFfYBlwh3NWqxBCCCFEu9Ks\nwZwQQgghhGha7jBmTgghhBBC1EOCOSGEEEIINybBnDiplFIBLX0O7kgp1c+5CooQQghxVBLM1UEp\nFaOU+l4pdV1Ln4u7crbhEuAhpVTnlj4fd6GU6qGU+gRTamdES5+PO1JKdVNKTVVKjVJKRbb0+bgj\npVS48wdFWEufiztRSkUqpS5QSvVu6XNxV0qpYKVUsPPfqqXPx11IMFeNMv4FbAf2AJ+38Cm5Fdcb\nTyl1L6ZcTBnwP0xtQHEUztfefOAPwA+znF2Oa19Lnps7UUo9h1mreTywEHhNKRXVsmflXpRSLwDb\ngFeBLUqpyUqpkBY+rVZPKTUX890xF9iklJqhlAp37pP38HFQSj0NbAauAdAyQ/O4STDnpJQ6F7PO\n2znAYK31JK11ZgufllvRWmvnh/4VwJ1a6wla6xVa68MtfW6tmVJqKiZwGwicp7W+CPOlMBrkA+14\nKaWmA0OBUVrrCzHljqKAM1r0xNyI88t0MOa1dy3wBvAwcH9Lnldrp5S6FPgTMNb593HgSuAFkPfw\nsSilwpRS7wEXAQXABUqpvs59EggfBwnmqvQEcoE3tNYblFLnKKXilVJxSqmeLX1ybuRaIExr/W+l\n1DCl1PtKqbeVUncrpWIAlFLyuqupP6ZW4jla65+VUqFAEeArH2TH5sxqemJWhlmntXatBvMFUA78\n1mIn5yacbRiK+TL9xLmc4l6t9XQgD7hHKWVv2bNs1a4CCrTW/9NaH9RaP4MJ5C5VSk0EUEp5tOgZ\ntm6ewD7gEeAuYABwmVLKUwLh49Nuv1SVUt613lxfAouAvznHLC3A/MJ6BVihlLqhBU6zVaujDQGK\ngYNKqWsxxaDTMa+z2zFFodFaVzTribYydbTbVK31e859ns5M5m5ggDPbKQFdLdXb0PlhH4x57QU7\nx3t1wryf/YC/K6UmtdzZtk71tGFPjgx+92PWxX60ec+wdao2nMRS7XoO5nPPWu3QLzCvwWcAtNbl\nzXyqrVa1NnR9Dh4Cntdaf6O1/hr4DtPDM6SFTtHttMtgTin1D+BrIEEpNVYp5a+1zgC+AdIAL8wL\n6SqgC/AjcJNSalhLnXNrU0cb+jl3BWLGe10DvKK1vldrfQPOLi9nV1i7zc7V0W42Z8DmWlrP9YG/\nDuimlOogv0xrquf9m4754uyO+RGRimnLGYAPJqCb3lLn3NrU0YYBWutdwAbgMaXUxc7j5gHRwIdA\nqFJqaEudc2uglLob+CuYH6VKKeV8f2YB3YAzXcc6v1PeB7RS6mbn7dv9D7NabVju/FumtT5Yg6tS\nBwAADWhJREFU7Xvh75glPcfKZIjj066+UJVS/kqprzDjGj4AgoCngBedh6wCngXitdabgGzni20W\ncArmzdquHaUN/+k85B1MW12GCUhcfsJ82Z6rlPJqb9m5Y732tNZlzr+uwC0f8Ac85UPMOEobvuQ8\n5B1gJPAz5kv0Uq31QswPi/8CY5RSvs182q3KcXwG3oD5MfaaUioPuBzTfi9jxh+2S0qpIUqp1Ziu\n0wlKqQHOXa4fYS8DPYArlFK2ajfdCCQBkdC+x87V14bVf9g7A2SL1non5gfEKMx7ul233fFoV8Ec\n0A/oBdygtX4D80KZB9yglJqktS4GvnQGctW7A3cCoc5Le1dfG16vlJrs7CL8h/PYc1030loXYT7s\n8rXWJe0wQKmv3W5SSsW5Dqr2wfYt5suzk3S1Vjraa2+i84dXBdAH2Or68HcGyhGYALm9t+XR2vBa\n5xrXF2ECuNFa69O01r9jghZPTJazXXF2nV4BJANTAQ/M2GC01qVKKavzc28uZjjJ+a7bOjPGXTHd\n/e2Wsw3HUncb1vfDfjZm6MRYpVRvpdQEpdRtzXC6bqm9BXNBmOzaFqh8ES3EzNia48wYFdVxu4nA\nJuCT5jrRVuxobTjb2e3wHLAGGK+UusY5Nqcf0AEzjqQ9/so6WrvNU84CwdU+2KyYX/QDndvbW3vV\n5WhtONf5pVqGCVailVI9AJRS/TFjwb7WWhe187Y81vvXyzmA/yet9Zpqt7sO0wW7srlPuKVprUuB\nBOAlrfVrwA+YHoY/uQ5xHvcE5od/vFJqvFLK6uyWLsQM4Wm3nG24kHrasPoPrGrZuSJMxngUZgzd\ne5gfa6IO7S2Y8wB2YLoAAdBaF2DSvn44+/GVMUgp1V8p9SowB1gMpDT/Kbc6R2tDXyDeufkeIBHT\nlfM/TDfrFkzqvD063teeq9vmVyCGdv6LvpZjteE05+YngDHAV0qpRZgfFpuo6kpsz47WhjaqXocW\npVRXZWakzwceBD7UWpe1x8ym1nqD1vpn59X/w9TQjFNKBTrbxLVay52YgC4BcADLMIHLquY+59bm\nGG2o6wjoemACuXDgI6Cj1vrNZj9xN9EugrlqL5K1mLTtUNegSqc9mKBjklLKw/nLfQzmBXQGcLHW\nenZ7/kV/Am043tmG67TWf8HMRnoJGKS1vs35C63dOMHXnsX5xeB6Xz4OfNVsJ9tKNeC1919gCmYm\n+nZgiNb6dq11SXOed2vSgNdhBSaDdzfmM9Cutf4XtO8ssbPnYSvwGXA6MAHA9drSWm/AdLUOB/4F\nDNRaT3WNiRVHbcPar6uHMV3W/bXWd2it85r3TN1LmwnmlFlGpa9SqqPzevVfj67p92mYDNtlwIWu\nnVrrQszU8iLAtZboP4GJWusRWutfm+EptLgmasNCwM91W631z1rrxc43b5vUhK89P+cHXYXWukRr\n/YQ2MwzbvCZ87fk7ty3TWr+otZ6utd7cTE+jRTXh69DfuXkt8Det9fC2/Bl4tHarlil3cZXSeAsz\nY3qMUqq789jTwczQ1Fqv0Vq/r7XecvKfQctr6jZ0mqa1jnaNYRdH5/bBnHNcwmuYD54PgfVKqTOc\naVvXB1iZUspHKXUR8DRwALhZVc1IAjOW5LDWOst5mwyt9drmfTYt4yS0YU57+PV+Etottz20W3Un\noQ2zm/s5tLST8f513qZUa91mh5acQLtZlVKXV7tu0WZ1oA+Azpj1p78FvlRKBbbQ02kRJ6kNg5zH\n5bbIk3JTbh3MKVOx/AugN2YK/V8wxVZrFGlUpq7NfuAa55flTMzMrG+VUo8rpV7BDPBtd+O5pA0b\nRtqt8aQNG0/asGFOsN3SMNmjAOc+1yD8HzAzzm8HDmOGkuQ049NoUSexDdvdD7ImobV22wtm/cDf\nMX3qrm0PAwurXX8MyMZMg7ZU294ReA4zUHU5cG5LPx9pQ/e5SLtJG7aGi7Rhs7WbqnX7MZiZlYmY\ncXEt/pykDdv3RTkb1W04xxRp57/HYiYpxGit9yilwjBTwJcBP2utE5z99b66npStMuUM2t2gfGnD\nEyft1njSho0nbdgwTdluSqnOwBXa1OprN6QNWy+36WZVzuVlqHnO32HWEfxKKbUU042Qgam2/bZS\n6k2gc30fYlBZ/6ZdkDZsGGm3xpM2bDxpw4Zp6nZzBjRp7SkIkTZ0Ay2dGjzWBdMXvx+Tjj3duc2z\n2v5QYBBm6ai/VNt+PmZ21nkt/Rxa+iJtKO0mbei+F2lDaTdpQ7kc69KqM3NKqcuA+4GPMYU/XXWO\nKmv2aLOMSjCmaOg7qqpG11rAC7NOaLslbdgw0m6NJ23YeNKGDSPt1njShu6lVQZzSlXWqNmLGZg7\nB/g7MEQpNcF5TPXaNWVAGBChq2bJXIVZcWB1s5x0KyNt2DDSbo0nbdh40oYNI+3WeNKGbqqlU4PV\nL5h1KINqbfN0/g3ELAGSUm2fxfn3dOBLzPTnx4G3gSxgeks/J2lD97hIu0kbtoaLtKG0m7ShXBr0\n/9fSJ+B8MVyN+RWwA1OnZhZm4CSAgspZt4OAQ8DjzuvWavfRHVNRejGwADilpZ+XtGHrv0i7SRu2\nhou0obSbtKFcGvX/2OInAIOBrZiF2ftjFnrOAF4FQpzHuH4d+ACPAiXV9vlU268w06Bb/HlJG7b+\ni7SbtGFruEgbSrtJG8ql0f+XLfgickX7f/n/9u4u1LKyjuP49ze+MNnQRSg2RGVqMU7S20V4IYMh\nTTAWRUF0oSV2aRT04oVkE1SEMRddJKQwMBGOoIRREg3GhEn0dhE4WRA6k+MU6NiVpr2g/y6edZzN\nYZRYz9l7n7XP9wOLffZ69tr7WT/2xf88az/rAU4Br5tp+xzwO+ArZznuUuAR4DBwBW149+plB2mG\n09nMzQw3w2aG5maGbhu1LW0CRA3fDOCttOHdmmk+SJvqvC/JboCcWeftOO2a/CeBY8NxW2IN1fXM\ncBxz62eG/cxwHHPrZ4arZ2HFXJK9Se5IckuSPTNNvwKuAnYOr9tWVf8E7qcN234Q2jpvSV6Tts7b\nt4GHaMuI7Kuqfy3qPJbJDMcxt35m2M8MxzG3fma4+uZezCXZmeTHwA+AHbSK/qfDlyvAEeAE8OXZ\n46rqCO1GhW+b2X0xsAe4qareX1WPzrv/m4EZjmNu/cywnxmOY279zHALmec1XOAC4BBtdsulM/sf\nAu4b/t4G3AC8COxZd/xh4Ogyrj9vls0Mzc0Mp7uZobmZodsitrmOzFXV87SZL9+vquNJzh+aHgB2\nDUO6LwH30u4yfVeSa9O8AbgcuHuefdzszHAcc+tnhv3McBxz62eGW8vajJb5fUByXg0LOSdJVVWS\ng7R71HxqZt922syYK4E/0G5EeBL4RFU9OddObnJmOI659TPDfmY4jrn1M8OtY+7F3Fk/NDkK3FtV\n3xuu22+r9gPLi4F30u5980RVHV545ybCDMcxt35m2M8MxzG3fma4mhZezCW5BPgt8JGq+s2wb3s5\nI+b/ZobjmFs/M+xnhuOYWz8zXF2LvDXJ2uK9VwPPz3yRbgPuSXL5ovoyVWY4jrn1M8N+ZjiOufUz\nw9V37qI+qM4MAb4P+GGSvcCdtOVAbqyqxxbVl6kyw3HMrZ8Z9jPDccytnxmuvoVeZh1+ZHkMuIw2\ny2Z/Vd2+sA6sADMcx9z6mWE/MxzH3PqZ4Wpbxm/mHgT+AnzR6/TjmOE45tbPDPuZ4Tjm1s8MV9cy\nirlzqurFhX7oijHDccytnxn2M8NxzK2fGa6updyaRJIkSRtjYbNZJUmStPEs5iRJkibMYk6SJGnC\nLOYkSZImzGJOkiRpwizmJOlVJHkgyaFl90OSXonFnCRtkCTXJKkkFy67L5K2Dos5SZKkCbOYk6RB\nkguSHEryXJKnkty6rv36JL9P8mySp5Pcl+SNQ9slwC+Gl54eRugODW1JckuSx5O8kORYkusXeGqS\nVpjFnCSdcQD4APBx4FrgPcCemfbzgf3Au4APARcC9wxtTw7HAbwD2Al8fnj+DeAzwM3AbuBbwJ1J\nrpvXiUjaOlzOS5KAJDuAfwA3VdXdM/tOAT+qqhvPcswu4M/Am6rqVJJraKNzF1XVM8NrXgs8A+yt\nqodnjv0O8Paq2jfXE5O08s5ddgckaZO4jDby9uu1HVX1XJJja8+TvJc2Mvdu4PVAhqY304q+s9kN\nbAd+lmT2v+fzgL9uVOclbV0Wc5LU5FUb2wjbEeDnwA3A07TLrA/TisBXsvZzlg8DJ9e1/XdUTyVp\nhsWcJDWP0Yqrq4Dj8HIBdyXwOLCLVrzdWlUnhvaPrXuP/wyP58zs+xPwb+AtVXV0br2XtGVZzEkS\nL19SPQjcnuQ08Hfgq5wpzE7SirLPJrkDuAL4+rq3eQIo4LokPwFeqKpnkxwADiQJ8EtgB61ofKmq\n7pr3uUlabc5mlaQzvkSbwHD/8PhHWvFFVZ0GPg18lDbath/4wuzBVfW3Yf83gaeA7w5NtwFfG97/\nUeBB2szXE/M8GUlbg7NZJUmSJsyROUmSpAmzmJMkSZowizlJkqQJs5iTJEmaMIs5SZKkCbOYkyRJ\nmjCLOUmSpAmzmJMkSZqw/wEkoDrN/DZfdwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot adjusted close over time, only for test set\n",
    "rcParams['figure.figsize'] = 10, 8 # width 10, height 8\n",
    "matplotlib.rcParams.update({'font.size': 14})\n",
    "\n",
    "ax = test.plot(x='date', y='adj_close', style='gx-', grid=True)\n",
    "ax = est_df.plot(x='date', y='est', style='rx-', grid=True, ax=ax)\n",
    "ax.legend(['test', 'predictions using lstm'], loc='upper left')\n",
    "ax.set_xlabel(\"date\")\n",
    "ax.set_ylabel(\"USD\")\n",
    "ax.set_xlim([date(2018, 4, 23), date(2018, 11, 23)])\n",
    "ax.set_ylim([130, 155])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save as csv\n",
    "test_lstm = est_df\n",
    "test_lstm.to_csv(\"./out/test_lstm.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Findings\n",
    "* On the test set, the RMSE is 1.164 and MAPE is 0.583% using N_opt=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
